U.S. patent application number 16/938598 was filed with the patent office on 2021-02-04 for method and system for computer-aided triage of stroke.
The applicant listed for this patent is Viz.ai Inc.. Invention is credited to Yuval Duchin, David Golan, Gil Levi, Christopher Mansi.
Application Number | 20210035306 16/938598 |
Document ID | / |
Family ID | 1000005341276 |
Filed Date | 2021-02-04 |
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United States Patent
Application |
20210035306 |
Kind Code |
A1 |
Mansi; Christopher ; et
al. |
February 4, 2021 |
METHOD AND SYSTEM FOR COMPUTER-AIDED TRIAGE OF STROKE
Abstract
A system for computer-aided triage includes a router, a remote
computing system, and a client application. A method for
computer-aided triage includes receiving a data packet associated
with a patient and taken at a point of care; checking for a
suspected condition associated with the data packet; in an event
that the suspected condition is detected, determining a recipient
based on the suspected condition; and transmitting information to a
device associated with the recipient.
Inventors: |
Mansi; Christopher; (San
Francisco, CA) ; Golan; David; (San Francisco,
CA) ; Levi; Gil; (San Francisco, CA) ; Duchin;
Yuval; (San Francisco, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Viz.ai Inc. |
San Francisco |
CA |
US |
|
|
Family ID: |
1000005341276 |
Appl. No.: |
16/938598 |
Filed: |
July 24, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62880227 |
Jul 30, 2019 |
|
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|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06T 7/11 20170101; G06T
5/50 20130101; G06T 2207/20084 20130101; G16H 10/20 20180101; G06T
2207/30016 20130101 |
International
Class: |
G06T 7/11 20060101
G06T007/11; G06T 5/50 20060101 G06T005/50; G16H 10/20 20060101
G16H010/20 |
Claims
1. A method for automatically detecting a potential ischemic
condition, the method comprising: at a computing system: receiving
a set of images associated with a patient and taken at the point of
care; processing the set of images, wherein processing the set of
images comprises: producing a mirror image version of each of the
set of images, thereby producing a mirrored set of images;
overlaying and aligning the mirrored set of images with the set of
images, thereby producing an overlaid set of images; segmenting an
ischemic region from the set of images based on a set of deep
learning models, wherein a set of inputs of the set of deep
learning models comprises the set of images and the set of overlaid
images; automatically detecting the potential ischemic condition
based on the ischemic region; compressing the set of images to
produce a compressed set of images; in response to detecting the
potential ischemic condition from the set of images, automatically:
determining, at the computing system, a specialist associated with
a treatment of the potential ischemic condition; transmitting a
notification and at least one image of the compressed set of images
to a recipient, wherein the recipient is the specialist, at a
client application executing on a user device associated with the
specialist; and receiving an input from the specialist at the
client application, wherein the input triggers an assignment of a
treatment of the patient to the specialist.
2. The method of claim 1, wherein the specialist is associated with
a second point of care remote from the first point of care.
3. The method of claim 1, further comprising determining a first
image of the at least one image of the compressed set of images,
wherein the first image is provided as a thumbnail with the
notification.
4. The method of claim 3, wherein the first image corresponds to a
maximum cross section of the ischemic region.
5. The method of claim 1, further comprising receiving a read
receipt at the computing system upon the specialist opening the
notification, wherein in an event that the read receipt is not
received within a predetermined time threshold, the method further
comprises transmitting the notification and the at least one image
to a second specialist at a second user device associated with the
second specialist.
6. The method of claim 1, further comprising checking for a set of
inclusion criteria of the set of images based on metadata
associated with the set of images.
7. The method of claim 1, further comprising, after detecting the
potential ischemic condition from the set of images, notifying a
second recipient associated with the potential ischemic
condition.
8. The method of claim 7, further comprising establishing
communication between the first and second recipients.
9. The method of claim 8, wherein the second recipient is a
principal investigator associated with a clinical trial.
10. The method of claim 1, further comprising implementing a
buffering protocol, wherein the buffering protocol comprises
transmitting a remainder of the compressed set of images to the
first recipient at a set of times later than a first time at which
the at least one image is transmitted.
11. The method of claim 1, wherein the set of images is
concurrently sent to a standard radiology workflow at the point of
care, wherein the method at least partially overlaps with the
standard radiology workflow.
12. A method for automatically detecting a potential ischemic
condition, the method comprising: at a computing system: receiving
a set of images associated with a patient; processing the set of
images with a deep learning model, wherein processing the set of
images comprises segmenting an ischemic region corresponding to the
ischemic condition, and wherein a set of inputs of the deep
learning model comprises the set of images and a mirrored version
of the set of images; in response to detecting the potential
ischemic condition from the set of images, automatically:
determining, at the computing system, a recipient associated with
the potential ischemic condition; transmitting a notification and
at least one image determined based on the set of images to the
recipient at a client application executing on a user device
associated with the recipient; and receiving an input from the
recipient at the client application.
13. The method of claim 12, wherein the ischemic condition is an
ischemic core.
14. The method of claim 12, wherein the input triggers an
assignment of a treatment of the patient to a care team associated
with the recipient.
15. The method of claim 14, wherein the recipient is a specialist
of the care team, wherein the care team is a stroke care team.
16. The method of claim 15, further comprising: transmitting the
notification and the at least one image to a second recipient at a
second client application executing on a second user device
associated with the second recipient, wherein the second recipient
is part of the stroke care team; and establishing a communication
interface between the first and second client applications.
17. The method of claim 12, wherein the at least one image is a
compressed version of an image of the set of images.
18. The method of claim 12, wherein the recipient is a principal
investigator associated with a clinical trial.
19. The method of claim 12, wherein a first image of the at least
one image corresponds to a maximum cross section of the hyperdense
region.
20. The method of claim 19, wherein the first image is provided as
a thumbnail with the notification.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of U.S. Provisional
Application No. 62/880,227, filed 30 Jul. 2019, which is
incorporated herein in its entirety by this reference.
TECHNICAL FIELD
[0002] This invention relates generally to the medical diagnostic
field, and more specifically to a new and useful system and method
for computer-aided triage in the medical diagnostic field.
BACKGROUND
[0003] In current triaging workflows, especially those in an
emergency setting, a patient presents at a first point of care,
where an assessment, such as imaging, is performed. The image data
is then sent to a standard radiology workflow, which typically
involves: images (equivalently referred to herein as instances)
being uploaded to a radiologist's queue, the radiologist reviewing
the images at a workstation, the radiologist generating a report,
an emergency department doctor reviewing the radiologist's report,
the emergency department doctor determining a specialist to
contact, and making a decision of how to treat and/or transfer the
patient to a 2.sup.nd point of care. This workflow is typically
very time-consuming, which increases the time it takes to treat
and/or transfer a patient to a specialist. In many conditions,
especially those involving stroke, time is extremely sensitive, as
it is estimated that in the case of stroke, a patient loses about
1.9 million neurons per minute that the stroke is left untreated
(Saver et al.). Further, as time passes, the amount and types of
treatment options available to the patient decrease.
[0004] In some instances, such as that of brain tissue injury
and/or death (e.g., ischemic core, ischemic penumbra, etc.), the
time until treatment is particularly critical, not only because the
brain tissue continues to experience greater injury/death as time
passes, but because the particular treatment option can be highly
dependent upon the amount of time that has passed, as this
determines any or all of: an amount of affected brain, a severity
of the affected brain, and a function of the affected brain.
[0005] Thus, there is a need in the triaging field to create an
improved and useful system and method for decreasing the time it
takes to determine a suspected condition and initiate treatment for
the patient.
BRIEF DESCRIPTION OF THE FIGURES
[0006] FIG. 1 is a schematic of a system for computer-aided
triage.
[0007] FIG. 2 is a schematic of a method for computer-aided
triage.
[0008] FIG. 3 depicts a variation of a method for computer-aided
triage.
[0009] FIG. 4 depicts a variation of a portion of a method for
computer-aided triage.
[0010] FIGS. 5A and 5B depict a variation of an application on a
user device.
[0011] FIG. 6 depicts a variation of a system for computer-aided
triage.
[0012] FIG. 7 depicts a variation of a method for computer-aided
triage.
[0013] FIG. 8 depicts a variation of the method.
[0014] FIG. 9 depicts a variation of the method involving
recommending the patient for a clinical trial.
[0015] FIG. 10 depicts a variation of a notification transmitted to
a device of a recipient.
[0016] FIG. 11 depicts a variation of a notification and subsequent
workflow of recommending a patient for a clinical trial.
[0017] FIG. 12 depicts a variation of the system.
[0018] FIG. 13 depicts variations of a client application executing
on first and second user devices.
DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0019] The following description of the preferred embodiments of
the invention is not intended to limit the invention to these
preferred embodiments, but rather to enable any person skilled in
the art to make and use this invention.
1. Overview
[0020] As shown in FIG. 1, a system 100 for computer-aided triage
includes a router, a remote computing system, and a client
application. Additionally or alternatively, the system 100 can
include any number of computing systems (e.g., local, remote),
servers (e.g., PACS server), storage, lookup table, memory, and/or
any other suitable components. Further additionally or
alternatively, the system can include any or all of the components,
embodiments, and examples as described in any or all of: U.S.
application Ser. No. 16/012,458, filed 19 Jun. 2018; and U.S.
application Ser. No. 16/012,495, filed 19 Jun. 2018; U.S.
application Ser. No. 16/688,721, filed 19 Nov. 2019; and U.S.
application Ser. No. 16/913,754, filed 26 Jun. 2020; each of which
is incorporated in its entirety by this reference.
[0021] As shown in FIG. 2, a method 200 for computer-aided triage
includes receiving a data packet associated with a patient and
taken at a first point of care S205; checking for a suspected
condition associated with the data packet S220; in an event that
the suspected condition is detected, determining a recipient based
on the suspected condition S230; and transmitting information to a
device associated with the recipient S250. Additionally or
alternatively, the method 200 can include any or all of:
transmitting data to a remote computing system S208; preparing a
data packet for analysis S210; determining a parameter associated
with a data packet; determining a treatment option based on the
parameter; preparing a data packet for transfer S240; receiving an
input from the recipient; initiating treatment of the patient;
transmitting information to a device associated with a second point
of care; aggregating data; and/or any other suitable processes.
Further additionally or alternatively, the method 200 can include
any or all of the processes, embodiments, and examples described in
any or all of: U.S. application Ser. No. 16/012,458, filed 19 Jun.
2018; and U.S. application Ser. No. 16/012,495, filed 19 Jun. 2018;
U.S. application Ser. No. 16/688,721, filed 19 Nov. 2019; and U.S.
application Ser. No. 16/913,754, filed 26 Jun. 2020; each of which
is incorporated in its entirety by this reference, or any other
suitable processes performed in any suitable order. The method 200
can be performed with a system 100 as described above and/or any
other suitable system.
2. Benefits
[0022] The system and method for computer-aided triage can confer
several benefits over current systems and methods.
[0023] In a first variation, the system and/or method confers the
benefit of reducing the time to match and/or transfer a patient
presenting with a condition (e.g., ischemia, ischemic core,
ischemic penumbra, ischemic stroke, etc.) to a specialist. In a
specific example, for instance, the average time between generating
a non-contrast dataset and notifying a specialist is reduced from
over 50 minutes to less than 8 minutes.
[0024] In a second variation, additional or alternative to those
described above, the system and/or method provides a parallel
process to a traditional workflow (e.g., standard radiology
workflow), which can confer the benefit of reducing the time to
determine a treatment option while having the outcome of the
traditional workflow as a backup in the case that an inconclusive
or inaccurate determination (e.g., false negative, false positive,
etc.) results from the method.
[0025] In a third variation, additional or alternative to those
described above, the system and/or method is configured to have a
high sensitivity (e.g., 87.8%, approximately 88%, between 81% and
93%, greater than 87%, etc.), which functions to detect a high
number of true positive cases and help these patients reach
treatment faster. In the event that this results in a false
positive, only a minor disturbance--if any--is caused to a
specialist, which affects the specialist's workflow negligibly
(e.g., less than 5 minutes), if at all. In a specific example, the
method is configured to have a sensitivity above a predetermined
threshold after a particular category of artifacts (e.g., motion
artifacts, metal artifacts, etc.) have been checked for.
[0026] In a fourth variation, additional or alternative to those
described above, the system and/or method confers the benefit of
minimizing the occurrence of false positive cases (e.g., less than
10% occurrence, less than 5% occurrence, less than, which functions
to minimize disturbances caused to specialists or other
individuals. This can function to minimize unnecessary disturbances
to specialists in variations in which specialists or other users
are notified on a mobile device upon detection of a potential brain
condition (e.g., ischemic core), as it can minimize the occurrence
of a specialist being alerted (e.g., potentially at inconvenient
times of day, while the specialist is otherwise occupied, etc.) for
false positives, while still maintaining a fallback in the standard
radiology workflow in the event that a true positive is missed. In
a set of specific examples, the method includes training (e.g.,
iteratively training) a set of deep learning models involved in
ischemic condition detection on images originally detected to be a
true positive but later identified as a false positive.
[0027] In a fifth variation, additional or alternative to those
described above, the system and/or method confers the benefit of
reorganizing a queue of patients, wherein patients having a certain
condition are detected early and prioritized (e.g., moved to the
front of the queue).
[0028] In a sixth variation, additional or alternative to those
described above, the system and/or method confers the benefit of
determining a patient condition (e.g., brain ischemia, ischemic
core, ischemic penumbra, dead brain volume above a predetermined
threshold, etc.) with a non-contrast (e.g., non-perfusion) scan. In
a specific example, this determination is made in less time (e.g.,
after the non-contrast scan has been performed, in absence of a
non-contrast scan, etc.) than it would take to perform and assess a
contrast scan (e.g., CTP scan).
[0029] In a seventh variation, additional or alternative to those
described above, the system and/or method confers the benefit of
determining actionable analytics to optimize a workflow, such as an
emergency room triage workflow.
[0030] In an eighth variation, additional or alternative to those
described above, the system and/or method confers the benefit of
determining (e.g., quantifying) an amount of brain that can
potentially be saved (e.g., is uninjured, is reversibly injured,
etc.), which can subsequently be used in future decision making
(e.g., performing an operation only when a percentage of brain
above a predetermined threshold can be saved and/or maintained).
Additionally or alternatively, the method can function to determine
which areas of the brain (e.g., functional regions, territories,
lobes, etc.) can be saved (and/or are not able to saved), which can
also be used in future decision making (e.g., anticipating and/or
scheduling a particular rehabilitation, refraining from drastic
intervention if a critical area of the brain has been damaged,
etc.).
[0031] In a ninth variation, additional or alternative to those
described above, the system and/or method confers the benefit of
detecting a brain ischemic condition (e.g., ischemia, ischemic
core, ischemic penumbra, ischemic stroke, etc.), which is
conventionally hard to detect, especially in
non-perfusion/non-contrast scans. In some examples, for instance,
the method leverages a natural symmetry of the brain between the
left and right halves to detect these subtle changes in a
non-perfusion scan. In specific examples, a mirror image of each
image is taken and used as an input, along with the images
(original images), to a machine learning model to identify the
subtle, hard-to-detect differences corresponding to an ischemic
condition (e.g., ischemic core, ischemic penumbra, ischemic stroke,
etc.). This can have advantages over conventional attempts at
solving this, which attempt to detect ischemic changes through
primarily thresholding the set of images based on a particular
pixel value threshold (e.g., a gray value), but variations
throughout the brain (and throughout different patients and/or
images) would either require a large amount of thresholds to be
applied, often inaccurately, and/or the results would not be
trustworthy.
[0032] In a tenth variation, additional or alternative to those
described above, the system and/or method confers the benefit of
automatically determining a potential ischemia (e.g., ischemic
core) and/or a parameter associated with the ischemia (e.g.,
percentage and/or amount of brain irreversibly damaged, brain
damage severity, brain damage location, brain function
corresponding to damaged region, etc.) in a set of non-contrast
scans and notifying a specialist prior to the identification of a
potential brain ischemia by a radiologist during a parallel
workflow. In conventional systems and methods, the contrast
distinction of contrast images is required to detect (e.g.,
accurately detect) ischemic conditions.
[0033] In an eleventh variation, additional or alternative to those
described above, the system and/or method confers the benefit of
recommending a patient for a clinical trial based on an automated
processing of a set of images (e.g., brain images) associated with
the patient.
[0034] Additionally or alternatively, the system and method can
confer any other benefit.
3. System
[0035] The system preferably interfaces with one or more points of
care (e.g., 1.sup.st point of care, 2.sup.nd point of care,
3.sup.rd point of care, etc.), which are each typically a
healthcare facility. A 1.sup.st point of care herein refers to the
healthcare facility at which a patient presents, typically where
the patient first presents (e.g., in an emergency setting).
Conventionally, healthcare facilities include spoke facilities,
which are often general (e.g., non-specialist, emergency, etc.)
facilities, and hub (e.g., specialist) facilities, which can be
equipped or better equipped (e.g., in comparison to spoke
facilities) for certain procedures (e.g., mechanical thrombectomy),
conditions (e.g., stroke), or patients (e.g., high risk). Patients
typically present to a spoke facility at a 1.sup.st point of care,
but can alternatively present to a hub facility, such as when it is
evident what condition their symptoms reflect, when they have a
prior history of a serious condition, when the condition has
progressed to a high severity, when a hub facility is closest,
randomly, or for any other reason. A healthcare facility can
include any or all of: a hospital, clinic, ambulances, doctor's
office, imaging center, laboratory, primary stroke center (PSC),
comprehensive stroke center (CSC), stroke ready center,
interventional ready center, or any other suitable facility
involved in patient care and/or diagnostic testing.
[0036] A patient can be presenting with symptoms of a condition, no
symptoms (e.g., presenting for routine testing), or for any other
suitable symptoms. In some variations, the patient is presenting
with one or more stroke symptoms (e.g., vessel occlusion symptoms),
such as, but not limited to: weakness, numbness, speech
abnormalities, and facial drooping. Typically, these patients are
then treated in accordance with a stroke protocol, which typically
involves an imaging protocol at an imaging modality, such as, but
not limited to: a non-contrast CT (NCCT) scan of the head, a CTA
scan of the head and neck, a CT perfusion (CTP) scan of the
head.
[0037] A healthcare worker herein refers to any individual or
entity associated with a healthcare facility, such as, but not
limited to: a physician, emergency room physician (e.g., orders
appropriate lab and imaging tests in accordance with a stroke
protocol), radiologist (e.g., on-duty radiologist, healthcare
worker reviewing a completed imaging study, healthcare working
authoring a final report, etc.), neuroradiologist, specialist
(e.g., neurovascular specialist, vascular neurologist,
neuro-interventional specialist, neuro-endovascular specialist,
expert/specialist in a procedure such as mechanical thrombectomy,
cardiac specialist, etc.), administrative assistant, healthcare
facility employee (e.g., staff employee), emergency responder
(e.g., emergency medical technician), or any other suitable
individual.
[0038] The image data can include computed tomography (CT) data
(e.g., radiographic CT, non-contrast CT, CT perfusion, etc.),
preferably non-contrast CT data (e.g., axial data, axial series of
slices, consecutive slices, etc.) but can additionally or
alternatively any other suitable image data. The image data is
preferably generated at an imaging modality (e.g., scanner at the
1.sup.st point of care), such as a CT scanner, magnetic resonance
imaging (MRI) scanner, ultrasound system, or any other scanner.
Additionally or alternatively, image data can be generated from a
camera, user device, accessed from a database or web-based
platform, drawn, sketched, or otherwise obtained.
[0039] In some variations, the image data includes a set of
non-perfusion CT images. While CT perfusion (CTP) images would
likely make any brain tissue injury and/or death (e.g., ischemic
penumbra, ischemic core, etc.) much more visible than in
non-perfusion images, the time required to perform a non-perfusion
scan is significantly less, which can provide more and better
options treatment options for the patient, as well as decrease a
total amount of irreversible tissue damage.
3.1 System--Router 110
[0040] The system 100 can include a router no (e.g., medical
routing system), which functions to receive a data packet (e.g.,
dataset) including instances (e.g., images, scans, etc.) taken at
an imaging modality (e.g., scanner) via a computing system (e.g.,
scanner, workstation, PACS server) associated with a 1.sup.st point
of care. The instances are preferably in the Digital Imaging and
Communications in Medicine (DICOM) file format, as well as
generated and transferred between computing system in accordance
with a DICOM protocol, but can additionally or alternatively be in
any suitable format. Additionally or alternatively, the instances
can include any suitable medical data (e.g., diagnostic data,
patient data, patient history, patient demographic information,
etc.), such as, but not limited to, PACS data, Health-Level 7 (HL7)
data, electronic health record (EHR) data, or any other suitable
data, and to forward the data to a remote computing system.
[0041] The instances preferably include (e.g., are tagged with)
and/or associated with a set of metadata, but can additionally or
alternatively include multiple sets of metadata, no metadata,
extracted (e.g., removed) metadata (e.g., for regulatory purposes,
HIPAA compliance, etc.), altered (e.g., encrypted, decrypted, etc.)
metadata, or any other suitable metadata, tags, identifiers, or
other suitable information.
[0042] The router 110 can refer to or include a virtual entity
(e.g., virtual machine, virtual server, etc.) and/or a physical
entity (e.g., local server). The router can be local (e.g., at a
1.sup.st healthcare facility, 2.sup.nd healthcare facility, etc.)
and associated with (e.g., connected to) any or all of: on-site
server associated with any or all of the imaging modality, the
healthcare facility's PACS architecture (e.g., server associated
with physician workstations), or any other suitable local server or
DICOM compatible device(s). Additionally or alternatively, the
router can be remote (e.g., locate at a remote facility, remote
server, cloud computing system, etc.), and associated with any or
all of: a remote server associated with the PACS system, a
modality, or another DICOM compatible device such as a DICOM
router.
[0043] The router 110 preferably operates on (e.g., is integrated
into) a system (e.g., computing system, workstation, server, PACS
server, imaging modality, scanner, etc.) at a 1.sup.st point of
care but additionally or alternatively, at a 2.sup.nd point of
care, remote server (e.g., physical, virtual, etc.) associated with
one or both of the 1.sup.st point of care and the 2.sup.nd point of
care (e.g., PACS server, EHR server, HL7 server), a data storage
system (e.g., patient records), or any other suitable system. In
some variations, the system that the router operates on is physical
(e.g., physical workstation, imaging modality, scanner, etc.) but
can additionally or alternatively include virtual components (e.g.,
virtual server, virtual database, cloud computing system,
etc.).
[0044] The router 110 is preferably configured to receive data
(e.g., instances, images, study, series, etc.) from an imaging
modality, preferably an imaging modality (e.g., CT scanner, MRI
scanner, ultrasound machine, etc.) at a first point of care (e.g.,
spoke, hub, etc.) but can additionally or alternatively be at a
second point of care (e.g., hub, spoke, etc.), multiple points of
care, or any other healthcare facility. The router can be coupled
in any suitable way (e.g., wired connection, wireless connection,
etc.) to the imaging modality (e.g., directly connected, indirectly
connected via a PACS server, etc.). Additionally or alternatively,
the router 100 can be connected to the healthcare facility's PACS
architecture, or other server or DICOM-compatible device of any
point of care or healthcare facility.
[0045] In some variations, the router includes a virtual machine
operating on a computing system (e.g., computer, workstation, user
device, etc.), imaging modality (e.g., scanner), server (e.g., PACS
server, server at 1.sup.st healthcare facility, server at 2.sup.nd
healthcare facility, etc.), or other system. In a specific example,
the router is part of a virtual machine server. In another specific
example, the router is part of a local server.
3.2 System--Remote Computing System 120
[0046] The system 100 can include a remote computing system 120,
which can function to receive and process data packets (e.g.,
dataset from router), determine a treatment option (e.g., select a
2.sup.nd point of care, select a specialist, etc.), interface with
a user device (e.g., mobile device), compress a data packet,
extract and/or remove metadata from a data packet (e.g., to comply
with a regulatory agency), or perform any other suitable
function.
[0047] Preferably, part of the method 200 is performed at the
remote computing system (e.g., cloud-based), but additionally or
alternatively all of the method 200 can be performed at the remote
computing system, the method 200 can be performed at a local
computing system (e.g., at a healthcare facility), any other
suitable computing system(s). In some variations, the remote
computing system 120 provides an interface for technical support
(e.g., for a client application) and/or analytics. In some
variations, the remote computing system includes storage and is
configured to store and/or access a lookup table, wherein the
lookup table functions to determine a treatment option (e.g.,
2.sup.nd point of care), a contact associated with the 2.sup.nd
point of care, and/or any other suitable information.
[0048] In some variations, the remote computing system 120 connects
multiple healthcare facilities (e.g., through a client application,
through a messaging platform, etc.).
[0049] In some variations, the remote computing system 120
functions to receive one or more inputs and/or to monitor a set of
client applications (e.g., executing on user devices, executing on
workstations, etc.).
3.3 System--Application 130
[0050] The system 100 can include one or more applications 130
(e.g., clients, client applications, client application executing
on a device, etc.), such as the application shown in FIGS. 5A and
5B, which individually or collectively function to provide one or
more outputs (e.g., from the remote computing system) to a contact.
Additionally or alternatively, the applications can individually or
collectively function to receive one or more inputs from a contact,
provide one or more outputs to a healthcare facility (e.g., first
point of care, second point of care, etc.), establish communication
between healthcare facilities, or perform any other suitable
function.
[0051] In some variations, one or more features of the application
(e.g., appearance, information content, information displayed, user
interface, graphical user interface, etc.) are determined based on
any or all of: the type of device that the application is operating
on (e.g., user device vs. healthcare facility device, mobile device
vs. stationary device), where the device is located (e.g., 1.sup.st
point of care, 2.sup.nd point of care, etc.), who is interacting
with the application (e.g., user identifier, user security
clearance, user permission, etc.), or any other characteristic. In
some variations, for instance, an application executing on a
healthcare facility will display a 1.sup.st set of information
(e.g., uncompressed images, metadata, etc.) while an application
executing on a user device will display a 2.sup.nd set of
information (e.g., compressed images, no metadata, etc.). In some
variations, the type of data to display is determined based on any
or all of: an application identifier, mobile device identifier,
workstation identifier, or any other suitable identifier.
[0052] The outputs of the application can include any or all of: an
alert or notification (e.g., push notification, text message, call,
email, etc.); an image set (e.g., compressed version of images
taken at scanner, preview of images taken at scanner, images taken
at scanner, etc.); a set of tools for interacting with the image
set, such as any or all of panning, zooming, rotating, window
leveling, scrolling, maximum intensity projection [MIP] (e.g.,
option to select the slab thickness of a MIP); changing the
orientation of 3D scan (e.g., changing between axial, coronal, and
sagittal views), showing multiple views of a set of images; a
worklist (e.g., list of patients presenting for and/or requiring
care, patients being taken care of by specialist, patients
recommended to specialist, procedures to be performed by
specialist, etc.); a messaging platform (e.g., HIPAA-compliant
messaging platform, texting platform, video messaging, etc.); a
telecommunication platform (e.g., video conferencing platform); a
directory of contact information (e.g., 1.sup.st point of care
contact info, 2.sup.nd point of care contact info, etc.); tracking
of a workflow or activity (e.g., real-time or near real-time
updates of patient status/workflow/etc.); analytics based on or
related to the tracking (e.g., predictive analytics such as
predicted time remaining in radiology workflow or predicted time
until stroke reaches a certain severity; average time in a
workflow, average time to transition to a second point of care,
etc.); or any other suitable output.
[0053] The inputs can include any or all of the outputs described
previously, touch inputs (e.g., received at a touch-sensitive
surface), audio inputs, optical inputs, or any other suitable
input. The set of inputs preferably includes an input indicating
receipt of an output by a contact. This can include an active input
from the contact (e.g., contact makes selection at application), a
passive input (e.g., read receipt), or any other input.
[0054] In one variation, the system 100 includes a mobile device
application 130 and a workstation application 130--both connected
to the remote computing system--wherein a shared user identifier
(e.g., specialist account, user account, etc.) can be used to
connect the applications (e.g., retrieve a case, image set, etc.)
and determine the information to be displayed at each application
(e.g., variations of image datasets). In one example, the
information to be displayed (e.g., compressed images,
high-resolution images, etc.) can be determined based on: the
system type (e.g., mobile device, workstation), the application
type (e.g., mobile device application, workstation application,),
the user account (e.g., permissions, etc.), any other suitable
information, or otherwise determined.
[0055] The application can include any suitable algorithms or
processes for analysis, and part or all of the method 200 can be
performed by a processor associated with the application.
[0056] The application preferably includes both front-end (e.g.,
application executing on a user device, application executing on a
workstation, etc.) and back-end components (e.g., software,
processing at a remote computing system, etc.), but can
additionally or alternatively include just front-end or back-end
components, or any number of components implemented at any suitable
system(s).
3.4 System--Additional Components
[0057] The system 100 and/or or any component of the system 100 can
optionally include or be coupled to any suitable component for
operation, such as, but not limited to: a processing module (e.g.,
processor, microprocessor, etc.), control module (e.g., controller,
microcontroller), power module (e.g., power source, battery,
rechargeable battery, mains power, inductive charger, etc.), sensor
system (e.g., optical sensor, camera, microphone, motion sensor,
location sensor, etc.), or any other suitable component.
3.5 System--Variations
[0058] In one variation, the system includes a router 110, which
operates at a computing system at a 1.sup.st point of care and
receives image data from an imaging modality. The router transmits
the image data to a remote computing system, wherein a series of
algorithms (e.g., machine learning algorithms) are performed at the
remote computing system, which determines a hypothesis for whether
or not a suspected condition (e.g., ischemic core) is present based
on the image data and/or any associated metadata. Based on the
determination, a contact is determined from a lookup table (e.g.,
in storage at the remote computing system), wherein the contact is
notified at a user device (e.g., personal device) and sent image
data through a client application executing on the user device. One
or more inputs from the contact at the application can be received
at the remote computing system, which can be used to determine a
next point of care for the patient.
[0059] Additionally or alternatively, any or all of the computing
can be performed at a local computing system (e.g., at the 1.sup.st
point of care), a computing system associated with a user device,
and/or any other suitable computing system.
4. Method
[0060] As shown in FIG. 2, a method 200 for computer-aided triage
includes receiving a data packet associated with a patient and
taken at a first point of care S205; checking for a suspected
condition associated with the data packet S220; in an event that
the suspected condition is detected, determining a recipient based
on the suspected condition S230; and transmitting information to a
device associated with the recipient S250. Additionally or
alternatively, the method 200 can include any or all of:
transmitting data to a remote computing system S208; preparing a
data packet for analysis S210; determining a parameter associated
with a data packet; determining a treatment option based on the
parameter; preparing a data packet for transfer S240; receiving an
input from the recipient; initiating treatment of the patient;
transmitting information to a device associated with a second point
of care; aggregating data; and/or any other suitable processes.
Further additionally or alternatively, the method 200 can include
any or all of the processes, embodiments, and examples described in
any or all of: U.S. application Ser. No. 16/012,458, filed 19 Jun.
2018; and U.S. application Ser. No. 16/012,495, filed 19 Jun. 2018;
U.S. application Ser. No. 16/688,721, filed 19 Nov. 2019; and U.S.
application Ser. No. 16/913,754, filed 26 Jun. 2020; each of which
is incorporated in its entirety by this reference, or any other
suitable processes performed in any suitable order. The method 200
can be performed with a system 100 as described above and/or any
other suitable system.
[0061] The method 200 can optionally be performed separate from but
in parallel with (e.g., contemporaneously with, concurrently with,
etc.) a standard radiology workflow (e.g., as shown in FIG. 3), but
can additionally or alternatively be implemented within a standard
workflow, be performed at a separate time with respect to a
standard workflow, or be performed at any suitable time.
[0062] The method 200 can be partially or fully implemented with
the system 100 or with any other suitable system.
[0063] The method 200 functions to improve communication across
healthcare facility networks (e.g., stroke networks, spokes and
hubs, etc.) and decrease the time required to transfer a patient
having a suspected time-sensitive condition (e.g., brain condition,
stroke, ischemic penumbra, ischemic core, hemorrhagic stroke,
ischemic stroke, large vessel occlusion (LVO), cardiac event,
trauma, etc.) from a first point of care (e.g., spoke,
non-specialist facility, stroke center, ambulance, etc.) to a
second point of care (e.g., hub, specialist facility, comprehensive
stroke center, etc.), wherein the second point of care refers to a
healthcare facility equipped to treat the patient. In some
variations, the second point of care is the first point of care,
wherein the patient is treated at the healthcare facility to which
he or she initially presents.
[0064] The method 200 can optionally function as a parallel
workflow tool, wherein the parallel workflow is performed
contemporaneously with (e.g., concurrently, during, partially
during) a standard radiology workflow (e.g., radiologist queue),
but can additionally or alternatively be implemented within a
standard workflow (e.g., to automate part of a standard workflow
process, decrease the time required to perform a standard workflow
process, etc.), be performed during a workflow other than a
radiology workflow (e.g., during a routine examination workflow),
or at any other suitable time.
[0065] The method 200 is preferably performed in response to a
patient presenting at a first point of care. The first point of
care can be an emergency setting (e.g., emergency room, ambulance,
imaging center, etc.), equivalently referred to herein as an acute
setting, or any suitable healthcare facility, such as those
described previously. The patient is typically presenting with (or
suspected to be presenting with) a time-sensitive condition, such
as a neurovascular condition (e.g., stroke, brain tissue injury,
brain tissue death, ischemic stroke, ischemia, ischemic core,
ischemic penumbra, occlusion, large vessel occlusion (LVO),
thrombus, aneurysm, etc.), cardiac event or condition (e.g.,
cardiovascular condition, heart attack, etc.), trauma (e.g., acute
trauma, blood loss, etc.), or any other time-sensitive (e.g.,
life-threatening) condition. In other variations, the method is
performed for a patient presenting to a routine healthcare setting
(e.g., non-emergency setting, clinic, imaging center, etc.), such
as for routine testing, screening, diagnostics, imaging, clinic
review, laboratory testing (e.g., blood tests), or for any other
reason.
[0066] Any or all of the method can be performed using any number
of machine learning (e.g., deep learning) modules. Each module can
utilize one or more of: supervised learning (e.g., using logistic
regression, using back propagation neural networks, using random
forests, decision trees, etc.), unsupervised learning (e.g., using
an Apriori algorithm, using K-means clustering), semi-supervised
learning, reinforcement learning (e.g., using a Q-learning
algorithm, using temporal difference learning), and any other
suitable learning style. Each module of the plurality can implement
any one or more of: a regression algorithm (e.g., ordinary least
squares, logistic regression, stepwise regression, multivariate
adaptive regression splines, locally estimated scatterplot
smoothing, etc.), an instance-based method (e.g., k-nearest
neighbor, learning vector quantization, self-organizing map, etc.),
a regularization method (e.g., ridge regression, least absolute
shrinkage and selection operator, elastic net, etc.), a decision
tree learning method (e.g., classification and regression tree,
iterative dichotomiser 3, C4.5, chi-squared automatic interaction
detection, decision stump, random forest, multivariate adaptive
regression splines, gradient boosting machines, etc.), a Bayesian
method (e.g., naive Bayes, averaged one-dependence estimators,
Bayesian belief network, etc.), a kernel method (e.g., a support
vector machine, a radial basis function, a linear discriminate
analysis, etc.), a clustering method (e.g., k-means clustering,
expectation maximization, etc.), an associated rule learning
algorithm (e.g., an Apriori algorithm, an Eclat algorithm, etc.),
an artificial neural network model (e.g., a Perceptron method, a
back-propagation method, a Hopfield network method, a
self-organizing map method, a learning vector quantization method,
etc.), a deep learning algorithm (e.g., a restricted Boltzmann
machine, a deep belief network method, a convolution network
method, a stacked auto-encoder method, etc.), a dimensionality
reduction method (e.g., principal component analysis, partial lest
squares regression, Sammon mapping, multidimensional scaling,
projection pursuit, etc.), an ensemble method (e.g., boosting,
boostrapped aggregation, AdaBoost, stacked generalization, gradient
boosting machine method, random forest method, etc.), V-nets,
U-nets, and/or any suitable form of machine learning algorithm.
Each module can additionally or alternatively be a: probabilistic
module, heuristic module, deterministic module, or be any other
suitable module leveraging any other suitable computation method,
machine learning method, or combination thereof.
[0067] In preferred variations, a set of U-nets and/or V-nets are
used in a segmentation process to identify and isolate brain matter
corresponding to a suspected patient condition (e.g., ischemic
core, ischemic penumbra, ischemic stroke, etc.).
[0068] Each module can be validated, verified, reinforced,
calibrated, or otherwise updated based on newly received,
up-to-date measurements; past measurements recorded during the
operating session; historic measurements recorded during past
operating sessions; or be updated based on any other suitable data.
Each module can be run or updated: once; at a predetermined
frequency; every time the method is performed; every time an
unanticipated measurement value is received; or at any other
suitable frequency. The set of modules can be run or updated
concurrently with one or more other modules, serially, at varying
frequencies, or at any other suitable time. Each module can be
validated, verified, reinforced, calibrated, or otherwise updated
based on newly received, up-to-date data; past data or be updated
based on any other suitable data. Each module can be run or
updated: in response to determination of an actual result differing
from an expected result; or at any other suitable frequency.
4.1 Method--Receiving a Data Packet Associated with a Patient and
Taken at a First Point of Care S205
[0069] The method 200 includes receiving data (e.g., data packet)
associated with a patient and taken at a first point of care S205,
which functions to collect data relevant to assessing a patient
condition.
[0070] The data is preferably received at a router 110, wherein the
router is in the form of a virtual machine operating on a computing
system (e.g., computer, workstation, quality assurance (QA)
workstation, reading workstation, PACS server, etc.) coupled to or
part of an imaging modality (e.g., CT scanner, MRI scanner, etc.),
or any other suitable router. Additionally or alternatively, data
can be received at a remote computing system (e.g., from an imaging
modality, from a database, a server such as a PACS server, an
internet search, social media, etc.), or at any other suitable
computing system (e.g., server) or storage site (e.g., database).
In some variations, for instance, a subset of the data (e.g., image
data) is received at the router while another subset of the data
(e.g., patient information, patient history, etc.) is received at a
remote computing system. In a specific example, the data subset
received at the router is eventually transmitted to the remote
computing system for analysis.
[0071] S205 is preferably performed in response to (e.g., after, in
real time with, substantially in real time with, with a
predetermined delay, with a delay of less than 10 seconds, with a
delay of less than 1 minute, at the prompting of a medical
professional, etc.) the data (e.g., each of a set of instances)
being generated at the imaging modality. Additionally or
alternatively, S205 can be performed in response to a set of
multiple instances being generated by the imaging modality (e.g.,
after a partial series has been generated, after a full series has
been generated, after a study has been generated, etc.), in
response to a metadata tag being generated (e.g., for an instance,
for a series, for a study, etc.), in response to a trigger (e.g.,
request for images), throughout the method (e.g., as a patient's
medical records are accessed, as information is entered a server,
as information is retrieved from a server, etc.), or at any other
suitable time.
[0072] S205 can be performed a single time or multiple times (e.g.,
sequentially, at different times in the method, once patient
condition has progressed, etc.). In one variation, each instance is
received (e.g., at a router, at a remote computing system, etc.)
individually as it is generated. In a second variation, a set of
multiple instances (e.g., multiple images, full series, etc.) are
received together (e.g., after a scan has completed, after a
particular anatomical component has been imaged, etc.).
[0073] The router (e.g., virtual machine, virtual server,
application running on the image sampling system or a computing
system connected to the image sampling system, etc.) can be
continuously `listening` (e.g., operating in a scanning mode,
receiving mode, coupled to or include a radio operating in a
suitable mode, etc.) for information from the imaging modality, can
receive information in response to prompting of a healthcare
facility worker, in response to a particular scan type being
initiated (e.g., in response to a head CTA scan being initiated),
or in response to any other suitable trigger.
[0074] Image data is preferably received at the router (e.g.,
directly, indirectly, etc.) from the imaging modality (e.g.,
scanner) at which the data was generated. Additionally or
alternatively, image data or any other data can be received from
any computing system associated with the healthcare facility's PACS
server, any DICOM-compatible devices such as a DICOM router, or any
other suitable computing system. The image data is preferably in
the DICOM format but can additionally or alternatively include any
other data format.
[0075] In addition to or alternative to image data, the data can
include blood data, electronic medical record (EMR) data,
unstructured EMR data, health level 7 (HL7) data, HL7 messages,
clinical notes, or any other suitable data related to a patient's
medical state, condition, or medical history.
[0076] The data preferably includes image data including a set of
one or more instances (e.g., images), which can be unorganized,
organized (e.g., into a series, into a study, a sequential set of
instances based on instance creation time, acquisition time, image
position, instance number, unique identification (UID), other
acquisition parameters or metadata tags, anatomical feature or
location within body, etc.), complete, incomplete, randomly
arranged, or otherwise arranged.
[0077] The image data is preferably received from (imaged at) a
scanner. The scanner can include any or all of: a computed
tomography (CT) scanner, a magnetic resonance imaging (MRI)
scanner, an ultrasound system, an X-Ray scanner, and/or any other
suitable imaging device (e.g., camera). The set of images can
include any or all of: CT images (e.g., non-contrast CT [NCCT]
images, contrast CT images, CT angiography images, etc.); MRI
images; X-ray images; ultrasound images; and/or any other suitable
images produced by any or all of these imaging devices or
others.
[0078] In preferred variations for detecting ischemic core, the set
of images preferably includes NCCT images of the patient's
head.
[0079] Each instance preferably includes (e.g., is tagged with) a
set of metadata associated with the image dataset, such as, but not
limited to: one or more patient identifiers (e.g., name,
identification number, UID, etc.), patient demographic information
(e.g., age, race, sex, etc.), reason for presentation (e.g.
presenting symptoms, medical severity score, etc.), patient history
(e.g., prior scans, prior diagnosis, prior medical encounters,
etc.), medical record (e.g. history of present illness, past
medical history, allergies, medications, family history, social
history, etc.), scan information, scan time, scan type (e.g.,
anatomical region being scanned, scanning modality, scanner
identifier, etc.), number of images in scan, parameters related to
scan acquisition (e.g., timestamps, dosage, gurney position,
scanning protocol, contrast bolus protocol, etc.), image
characteristics (e.g., slice thickness, instance number and
positions, pixel spacing, total number of slices, etc.), or any
other suitable information.
[0080] In some variations, any or all of the data (e.g., image
data) is tagged with metadata associated with the standard DICOM
protocol.
[0081] In some variations, one or more tags is generated and/or
applied to the data after the data is generated at an imaging
modality. In some examples, the tag is an identifier associated
with the 1.sup.st point of care (e.g., 1.sup.st point of care
location, imaging modality identifier, etc.), which can be
retrieved by a 2.sup.nd point of care in order to locate the
patient (e.g., to enable a quick transfer of the patient, to inform
a specialist of who to contact or where to reach the patient,
etc.).
[0082] Additionally or alternatively, image data can be received
without associated metadata (e.g., metadata identified later in the
method, dataset privately tagged later with metadata later in the
method, etc.).
[0083] In a first set of variations for detecting an ischemic
condition (e.g., ischemic core, ischemic penumbra, ischemic stroke,
etc.), metadata of images are checked for metadata inclusion
criteria, including/indicating any or all of: a CT scan of the
head, a non-contrast CT scan of the head, an axial series, a slice
thickness within a supported range, the absence of missing slices,
aligned instance numbers and positions, patient age above a minimum
threshold (e.g., between 0 and 10, between 10 and 20, between 20
and 30, above 30, etc.), consistent pixel spacing, a total number
of slices within a predetermined range (e.g., between 18 and 25),
and/or any other suitable metadata inclusion criteria.
[0084] Data can be received (e.g., at the router) through a wired
connection (e.g., local area network (LAN) connection), wireless
connection, or through any combination of connections and
information pathways.
[0085] In a first variation (e.g., for a patient presenting with
symptoms of an ischemic stroke, ischemic core, ischemic penumbra,
etc.), S205 includes receiving a non-perfusion CT (NCCT) scan of a
patient's head. In a preferred specific example of this variation,
the non-perfusion CT scan includes an axial series of image
instances (equivalently referred to herein as slices), each of the
slices corresponding to an axial thickness between 2.5 and 5
millimeters, wherein the axial series includes no missing slices
and wherein the slices are properly ordered (e.g., instance numbers
and positions aligned).
4.2 Method--Transmitting Data to a Remote Computing System S208
[0086] The method can include transmitting data to a remote
computing system (e.g., remote server, PACS server, etc.) S208,
which functions to enable remote processing of the data, robust
process, or fast processing (e.g., faster than analysis done in
clinical workflow, faster than done in a standard radiology
workflow, processing less than 20 minutes, less than 10 minutes,
less than 7 minutes, etc.) of the dataset.
[0087] The data (e.g., image data, image data and metadata, etc.)
is preferably transmitted to a remote computing system from a
router (e.g., virtual machine operating on a scanner) connected to
a computing system (e.g., scanner, workstation, PACS server, etc.)
associated with a healthcare facility, further preferably where the
patient first presents (e.g., 1.sup.st point of care), but can
additionally or alternatively be transmitted from any healthcare
facility, computing system, or storage site (e.g., database).
[0088] Each instance (e.g., image) of the dataset (e.g., image
dataset) is preferably sent individually as it is generated at an
imaging modality and/or received at a router, but additionally or
alternatively, multiple instances can be sent together after a
predetermined set (e.g., series, study, etc.) has been generated,
after a predetermined interval of time has passed (e.g., instances
sent every 10 seconds), upon the prompting of a medical
professional, or at any other suitable time. Further additionally
or alternatively, the order in which instances are sent to a remote
computing system can depend one or more properties of those
instances (e.g., metadata). S208 can be performed a single time or
multiple times (e.g., after each instance is generated).
[0089] S208 can include transmitting all of the dataset (e.g.,
image dataset and metadata), a portion of the dataset (e.g., only
image dataset, subset of image dataset and metadata, etc.), or any
other information or additional information (e.g., supplementary
information such as supplementary user information).
[0090] The data is preferably transmitted through a secure channel,
further preferably through a channel providing error correction
(e.g., over TCP/IP stack of 1.sup.st point of care), but can
alternatively be sent through any suitable channel.
[0091] S208 can include any number of suitable sub-steps performed
prior to or during the transmitting of data to the remote computing
system. These sub-steps can include any or all of: encrypting any
or all of the dataset (e.g., patient information) prior to
transmitting to the remote computing system, removing information
(e.g., sensitive information), supplementing the dataset with
additional information (e.g., supplementary patient information,
supplemental series of a study, etc.), compressing any or all of
the dataset, or performing any other suitable process.
4.3 Method--Preparing a Data Packet for Analysis S210
[0092] The method 200 preferably includes preparing a data packet
S210 for analysis (e.g., pre-processing), which can function to
pre-process any or all of the data packet, eliminate an irrelevant
(e.g., incorrectly labeled, irrelevant anatomical region, etc.) or
unsuitable data packet from further analysis, produce one or more
inputs for future processes in the method (e.g., an overlaid
registered image), or perform any other suitable function.
[0093] S210 can optionally include resizing one or more of the set
of instances, which can function to reduce an amount of data being
processed in future processes. Additionally, resizing can function
to shorten the time of transfer of data between points of the
system (e.g., from a virtual machine to a remote computing system,
from a remote computing system to a mobile device and/or
workstation, etc.), and therefore subsequently reduce a total time
required to process the data. Resizing the set of instances can be
performed in accordance with (e.g., as part of, contemporaneously
with, based on the results of, etc.) one or more registration
processes, such as--but not limited to--any or all of the
registration processes described below (e.g., a first set of
registration processes, a second set of registration processes,
etc.). Additionally or alternatively, the set of instances can be
resized to particular image size (e.g., predetermined image size,
dynamically determined image size, through downsampling, through
lossless compression, through lossy compression, etc.). In one
variation, for instance, S210 includes resizing each of the set of
instances from 512.times.512 to 256.times.256 or from 512.times.512
to 96.times.96.
[0094] Additionally or alternatively, S210 can include cropping one
or more of the set of images (e.g., according to a predetermined
crop size, to emphasize a particularly relevant region, to remove
an unwanted region, to increase a uniformity of the region shown in
a set of images, etc.), straightening out one or more of the set of
images (e.g., rotating a portion of the image, translating a
portion of the image, by registering each of a set of images with
its mirror image, etc.), or otherwise pre-processing the set of
images.
[0095] S210 can optionally include one or more registration
processes (e.g., image registration processes), which function to
align, scale, calibrate, or otherwise adjust the set of images.
Alternatively, the images can proceed to future processes in
absence of any registration processes. The registration process(es)
can be performed through registering any or all of the set of
images (e.g., soft matter regions extracted from set of images) to
a reference set of images (e.g., reference series); additionally or
alternatively, any or all of the registration process(es) can be
performed in absence of comparing the present set of instances with
a reference set of instances (e.g., by comparing a first region of
one instance with a second region of the same instance, by
comparing a right brain hemisphere of the instance with a left
brain hemisphere of the same instance, by comparing one instance of
the set of images with another instance of the set of images, by
comparing an instance with a mirror image of itself, etc.).
[0096] The registration can optionally be performed in response to
a data packet being filtered through a set of exclusion criteria.
Additionally or alternatively, registration can be performed prior
to or in absence of filtering, multiple times throughout the
method, prior to filtering through a set of exclusion criteria, or
at any other suitable time.
[0097] S210 can optionally include a first type of registration
process, equivalently referred to herein as reference registration,
wherein the set of images is registered against a reference set of
images, which functions to align and/or scale the set of images.
Additionally, this first registration process can function to
identify a left and right hemisphere of the brain. The first
registration process can be performed in response to receiving the
set of images (e.g., prior to checking for a particular brain
condition, prior to a second set of registration processes, etc.),
after a set of exclusion criteria have been checked or in response
to any other process, and/or at any other suitable time.
Alternatively, the method can be performed in absence of a first
set of registration processes.
[0098] The reference registration can be any or all of:
intensity-based, feature-based, or otherwise configured, and can
include any or all of: point-mapping, feature-mapping, or any other
suitable process. The reference set of instances is preferably
determined from a training set but can alternatively be determined
from any suitable dataset, computer-generated, or otherwise
determined. The reference series can be selected for any or all of
orientation, size, three-dimensional positioning, clarity,
contrast, or any other feature. In one variation, a references
series is selected from a training set, wherein the reference
series is selected based on a feature parameter (e.g., largest
feature size such as largest skull dimension, smallest feature
size, etc.) and/or a degree of alignment (e.g., maximal alignment,
alignment above a predetermined threshold, etc.). Additionally or
alternatively, any other criteria can be used to determine the
reference series, the reference series can be randomly selected,
formed from aggregating multiple sets of instances (e.g., multiple
patient series), or determined in any other suitable way. In some
variations, the registration is performed with one or more
particular software packages (e.g., SimpleElastix). In a specific
example, the registration is performed through affine registration
(e.g., in SimpleElastix) with a set of predetermined (e.g.,
default) parameters and iterations (e.g., 4096 iterations, between
3000 and 5000 iterations, above 1000 iterations, above 100
iterations, etc.) in each registration step.
[0099] S210 preferably includes a second type of registration
processes, equivalently referred to herein as intra-image
registration, which functions to enable a comparison between
multiple parts of a single image. This can enable, for instance, a
comparison between a right brain hemisphere (e.g., in size,
position, angle, etc.) and a left brain hemisphere from the same
image instance. This can be used (e.g., at a later process in the
method) to compare (e.g., map to) an area of injured tissue (e.g.,
ischemic penumbra, ischemic core, etc.) in a first brain region
with a corresponding area in a second brain region (e.g., a mirror
image region, an opposing region, etc.). In a first variation, for
instance, for a set of one or more images indicating a potential
ischemia in a left hemisphere, the area of injured tissue in the
left hemisphere can be compared with a corresponding tissue region
(e.g., horizontally opposing in an aligned brain image) in a right
hemisphere (e.g., to determine if there is injured tissue, to
confirm that there is injured tissue, to quantify or otherwise
assess an amount of injured tissue, etc.). In a second variation,
for instance, for a set of one or more images indicating a
potential ischemia in a right hemisphere, the area of injured
tissue in the right hemisphere can be compared with a corresponding
tissue region (e.g., non-injured region) in the left
hemisphere.
[0100] In preferred variations (e.g., as shown in FIG. 4), a
registration process includes producing a second input for
analysis, which can include any or all of: a mirror image of the
each of the set of images (e.g., after the set of images have been
registered with a first set of registration processes, the original
set of images, etc.), an overlaid image including the image
superimposed with (and optionally aligned with) its mirror image, a
registered mirror image (e.g., taken as a mirrored version of the
registered original image). The mirror image is preferably received
concurrently (e.g., at the same time, at an overlapping time,
immediately after, immediately before, etc.) with its corresponding
non-mirror image (e.g., original image, registered original image,
etc.) to on or more deep learning models, but can additionally or
alternatively be received at different times (e.g., after). Having
mirror images for comparison can ultimately function to enable
subtle visual differences (e.g., a color difference below a
predetermined threshold, a sulcal dimension difference [such as a
difference in spacing between adjacent sulci] below a predetermined
threshold, a "texture" difference below a predetermined threshold,
etc.) between regions of the image (e.g., between corresponding
regions in the left and right hemispheres) to be identified (e.g.,
with non-contrast scans). In these variations, for instance, a
machine learning model can be trained and/or used to better detect
subtle changes in brain matter (e.g., from non-contrast images)
corresponding to an ischemic condition by receiving mirror images
in addition to the set of images, which effectively enables ratios
in contrast between corresponding left and right voxels to be
utilized to determine ischemic regions. In specific examples, the
mirror images are overlaid and aligned with the corresponding
original images, wherein the original set of images and overlaid
images are received as an input to the machine learning
model(s).
[0101] Additionally or alternatively, and other suitable input(s)
can be received
[0102] A mirror image of each image is preferably formed by taking
a mirror image about a central axis (e.g., y-axis as shown in FIG.
4) of the image (e.g., prior to a first set of registrations steps,
in absence of a first set of registration steps, after the set of
images has been straightened out, in absence of the set of images
being straightened out, etc.). Additionally or alternatively, a
mirror image can be taken about an anatomical line (e.g., cerebral
fissure) or any other suitable axis of the set of images. In some
variations, a mirror image is produced based on an unregistered
original image. In additional or alternative variations, a first
mirror image is produced, which is used to register the original
image, and then the mirror image is updated to be registered based
on the registered original image (e.g., formed from the mirror
image of the registered image). In specific examples, the
registered mirror image is then overlaid with the registered
original image. Additionally or alternatively, the original image
and its mirror image can be registered together, not registered,
not overlaid, and/or otherwise produced and/or modified.
[0103] Each image is preferably then superimposed with its mirror
image, forming a superimposed image (equivalently referred to
herein as the overlaid image). The superimposed image can then be
registered to align the image with its mirror image in the
superimposed image, forming a registered superimposed image
(equivalently referred to herein as the registered overlaid image).
This functions to align the left and right hemispheres, enable a
mapping of a region in a first brain hemisphere to a corresponding
region in the opposing hemisphere, straighten the overall scan,
resize one or more regions of the scan (e.g., to correct for a
nonzero z-angle of the patient in the scanner), and/or perform any
other function. Alternatively, the image and its mirror image can
remain separate (e.g., and be processed independently).
[0104] Additional or alternative to variations in which a mirror
image is produced for each image, an intra-image registration
process can include separating (e.g., dividing) each of the set of
images (e.g., after a first set of image registration processes, in
absence of a first set of image registration processes, etc.) into
multiple parts, such as the left and right hemispheres (e.g.,
separating each image along a cerebral fissure). The hemispheres
can be identified through any or all of: a first set of
registration steps, a segmentation process, a windowing process
(e.g., based on Hounsfield unit values), a geometrical division of
each image (e.g., division along a centerline), or any other
suitable process. The multiple parts of each image can then be
processed in any suitable way (e.g., separately, together, etc.)
for any remainder processes of the method. In a specific example,
for instance, each of a set of brain images is divided into left
and right hemispheres (e.g., separated along the longitudinal
fissure, separated along a y-axis, separated along a y-axis after
the image has been straightened out, etc.), wherein the set of left
hemispheres and the set of right hemispheres are processed
separately and the results compared.
[0105] S210 can optionally include windowing (equivalently referred
to herein as clipping) the image data, which can function to
increase the image contrast of a predetermined (e.g., desired)
range (window) of pixel/voxel values (e.g., Hounsfield units),
eliminate irrelevant information (e.g., information not
corresponding to regions of interest/potential user conditions)
from further processing, and/or perform any other suitable
function. The threshold values (e.g., HU values) determining the
window range can optionally be determined based on any or all of:
the type of scan (e.g., contrast, non-contrast, CT, NCCT, MRI,
etc.), the body region scanned (e.g., head), suspected condition
(e.g., ischemic core, ischemic penumbra, ischemic stroke, etc.),
patient demographics and/or metadata (e.g., age, gender, etc.),
and/or any other suitable information. Additionally or
alternatively, the threshold values can be constant for all
images.
[0106] In some variations (e.g., for detecting brain conditions),
the range of HU values that are retained for processing ranges from
a Hounsfield unit just below that corresponding to soft matter to a
Hounsfield unit just above that corresponding to bone. Additionally
or alternatively, a window can be shifted with respect to this
(e.g., anything below bone), narrowed with respect to this (e.g.,
only encompassing brain tissue), broadened with respect to this, or
otherwise selected.
[0107] In specific examples, the information retained has HU values
between 1000 and 2000.
[0108] Windowing the data can be performed after one or more
registration processes, prior to one or more registration
processes, during one or more registration processes, at another
time, or at any combination of times. The window preferably ranges
from a Hounsfield unit just below that corresponding to soft matter
to a Hounsfield unit just above that corresponding to bone.
Additionally or alternatively, a window can be shifted with respect
to this (e.g., anything below bone), narrowed with respect to this
(e.g., only encompassing brain tissue), broadened with respect to
this, or otherwise selected.
[0109] S210 can further include an effective and/or actual removal
(e.g., assignment of a pixel value of zero, actual removal through
cropping, etc.) of one or more regions of the image, which can be
performed after and/or based on a windowing process (e.g., as
described above), but can additionally or alternatively be
performed prior to a windowing process, in absence of a windowing
process, in a later process (e.g., after a segmentation process),
or otherwise performed. In one variation, a set of "white" regions
(e.g., regions having a pixel/voxel value of 255, regions having a
pixel/voxel value above a predetermined threshold, etc.) are
assigned a pixel/voxel value of zero, which--in combination with a
windowing process as described above--can function to result in the
extraction of only soft matter (e.g., brain matter, blood, cerebral
fluid, etc.); this can equivalently be referred to herein as skull
stripping. Additionally or alternatively, any other regions can be
effectively and/or actually removed in any suitable way (e.g.,
through photomasks, dilation, erosion, etc.).
[0110] S210 can also optionally include the normalization of any or
all of the image data, which can function to enable the comparison
of multiple images with each other, the comparison of one series
with a different series (e.g., the series of a previously-taken
brain scan, the series of one patient with a reference series, the
series of a first patient that that of a second patient, etc.), or
perform any other suitable function, such as faster training
convergence of deep learning models in processing. In one
variation, the set of voxels in the image data are normalized such
that the voxels have a predetermined mean Hounsfield unit value
(e.g., 24.5 HU, less than 24.5 HU, greater than 24.5 HU, etc.) and
a predetermined standard deviation Hounsfield unit value (e.g.,
39.5 HU, less than 39.5 HU, greater than 39.5 HU, etc.). In
specific examples, the image data is normalized by dividing by a
fixed number. Additionally or alternatively, the image data can be
normalized in any other suitable way with respect to any suitable
parameter and/or the method can be performed in absence of a
normalization process.
[0111] S210 can optionally include organizing the set of images
(e.g., instances, slices, scans, etc.), preferably into a series,
but additionally or alternatively into a study, or any other
suitable grouping of images. The organization is preferably
performed in response to generating a set of images (e.g., at an
imaging modality), but can additionally or alternatively be
performed in response to receiving a set of instances at a location
(e.g., router, remote computing system, server such as a PACS
server, etc.), at the request of an individual (e.g., healthcare
worker), in response to a trigger, in response to any other
pre-processing step, or at any other suitable time.
[0112] In some variations, the method includes excluding a data
packet (e.g., set of instances) from further processing if one or
more of a set of metadata are not satisfied, such as, but not
limited to, the metadata listed above.
[0113] In a specific example, a bone mask is determined and defined
as a set of voxels having an HU value above a predetermined
threshold (e.g., 750 HU, 700 HU, 800 HU, between 600 HU and 900 HU,
etc.). The bone mask is then dilated with a series of kernels of
increasing size until it completely encloses a set of voxels of low
HU values (e.g., less than the predetermined threshold), thereby
defining a soft matter mask. The soft matter mask is dilated to
compensate for the dilation of the bone mask. If the process of
defining the soft matter mask fails, this can indicate that the
skull has undergone a craniotomy, which in some cases can be used
in determining a diagnosis, informing a contact or second point of
care, or in any other point in the method. Once the soft matter
mask is dilated, the mask can then be applied to the set of
instances (e.g., organized set of instances, series, etc.), and the
HU value of voxels outside of the mask is set to zero.
[0114] In a first set of variations, S210 includes producing a
mirror image of each of the set of images and overlaying and/or
registering each of the set of images with its mirror image.
Additionally or alternatively, S210 can include resizing each of a
set of NCCT images to a predetermined uniform size (e.g.,
256.times.256); windowing the NCCT images (e.g., to remove bone
matter); and/or any other suitable processes.
4.4 Method--Checking for a Suspected Condition Associated with the
Data Packet S220
[0115] The method 200 includes checking for a suspected condition
and optionally one or more parameters of the suspected condition
(e.g., as shown in FIG. 13) associated with the data packet S220,
which functions to determine a region of image data proposed to be
affected with a patient condition (e.g., brain ischemia) and inform
the remaining processes of the method. Additionally or
alternatively, S220 can function to reduce the time to transfer a
patient to a second point of care, halt progression of the
condition, or perform any other suitable function. S220 is
preferably fully performed at a remote computing system (e.g.,
remote server, cloud-based server, etc.), further preferably a
remote computing system having a graphics processing unit (GPU),
but can additionally or alternatively be partially performed at any
suitable remote computing system, be partially or fully performed
at a local computing system (e.g., workstation), server (e.g., PACS
server), at a processor of a user device, or at any other system.
S220 is preferably partially or fully performed using software
including one or more algorithms, further preferably one or more
multi-step algorithms containing steps that are either trained
(e.g., trained through machine learning, trained through deep
learning, continuously trained, etc.) or non-trained (e.g.,
rule-based image processing algorithms or heuristics). Additionally
or alternatively, any software can be implemented.
[0116] S220 preferably includes identifying (e.g., locating,
isolating, measuring, quantifying, etc.) and optionally segmenting
an affected brain region (e.g., injured tissue, ischemic tissue,
ischemic core tissue, ischemic penumbra tissue, etc.) within one or
more of the set of images, thereby indicating a brain condition
(e.g., ischemia, ischemic stroke, etc.) or at least a potential
brain condition (e.g., ischemia, ischemic core, ischemic stroke).
This can be performed through any number of computer vision
techniques, such as object recognition, object identification,
object detection, or any other form of image analysis.
[0117] In cases of brain ischemia (e.g., ischemic core), the
affected brain region can include and/or be characterized by any or
all of: hypodensity, which can be depicted as a dark region in a
brain scan (e.g., region having a pixel value [e.g., HU value]
above a predetermined threshold, region having a pixel value
exceeding a neighboring pixel value by a predetermined threshold,
etc.); abnormal texture, which can be depicted as a loss of
delineation in a region of the brain scan (e.g., region having an
overall contrast [e.g., range of HU values] below a predetermined
threshold, region having a spacing between detected features above
a predetermined threshold, region having an expected feature [e.g.,
gyrus] missing, etc.); sulcal effacement, which can be depicted as
a decreased fluid volume of one or more sulci (e.g., region having
a distance between adjacent gyri below a predetermined threshold);
or any other suitable feature.
[0118] As such, identifying an affected brain region (e.g.,
hypodense brain region) can include identifying (e.g., indirectly
with a machine learning model, with a classically programmed model,
etc.) image features associated with an ischemic condition based on
a trained machine learning model. These features can include a
pixel value and/or Hounsfield unit value outside of a "normal"
range (e.g., above a predetermined threshold value, below a
predetermined threshold value, outside a range of predetermined
values, etc.). This can include, for instance, any or all of:
identifying an image region having a pixel value above a
predetermined threshold, identifying a region having a Hounsfield
unit value above a predetermined threshold, identifying a region
having a pixel value below a predetermined threshold, identifying a
region having a Hounsfield unit value below a predetermined
threshold, identifying a region which differs in pixel value from
adjacent regions by a value above a predetermined threshold,
identifying a region which differs in Hounsfield unit value from
adjacent regions by a value above a predetermined threshold, or any
other suitable region having defined by any suitable features.
Additionally or alternatively, identifying an affected brain region
can include identifying a region (e.g., of a uniform size, of a
size above a predetermined threshold, etc.) having a delineation
parameter below a predetermined threshold (e.g., by detecting a
change in delineation with respect to an adjacent region above a
predetermined threshold, by detecting a set of lines in the brain
scan having a length below a predetermined threshold, etc.).
Further additionally or alternatively, identifying an affected
brain region can include identifying a region in which a set of
sulci have terminated (e.g., uniformly terminated, suddenly
terminated, gradually terminated, etc.) and/or experienced an
abnormal spacing (e.g., through detecting a spacing between
adjacent sulci below a predetermined threshold, through detecting a
spacing between adjacent gyri below a predetermined threshold,
through detecting a spacing between adjacent sulci above a
predetermined threshold, through detecting a spacing between
adjacent gyri above a predetermined threshold, etc.). Additionally
or alternatively, identifying an affected brain region can include
any other suitable processing of the set of images.
[0119] The image region can optionally be compared with one or more
thresholds, the one or more thresholds can be any or all of:
predetermined (e.g., constant, based on one or more anatomical
values, based on one or more physiological values, based on an
algorithm, based on an average value from a dataset, based on a
maximum value from a dataset, based on a minimum value from a
dataset, etc.), dynamically determined (e.g., specific to the user,
based on a value in a corresponding region in the opposing
hemisphere, based on a value found in the image dataset, etc.), any
combination of predetermined and dynamically determined, or
otherwise determined (e.g., without a set of thresholds).
[0120] Determining that a proposed brain region is potentially
affected (e.g., and suitable for future processing) can require any
or all of: the satisfaction of one of the above criteria (e.g.,
exceeding a threshold), the satisfaction of a predetermined number
(e.g., 2 or more, 3 or more, all, etc.) of the above criteria, the
presence of any other suitable criteria, or be otherwise
determined.
[0121] Identifying an affected brain region (e.g., region of tissue
death) preferably includes image segmentation (e.g., prior to
comparing an affected region with a threshold), wherein the
segmentation can include any or all of: thresholding, clustering
methods, dual clustering methods, compression-based methods,
histogram-based methods, region-growing methods, partial
differential equation-based methods, variational methods, graph
partitioning methods, watershed transformations, model based
segmentation, multi-scale segmentation, semi-automatic
segmentation, trainable segmentation, or any suitable form of
segmentation. The method can additionally or alternatively include
any number of segmentation post-processing steps, such as
thresholding, connectivity analyses, or any other processing. The
segmentation is preferably performed with a deep learning module
including a convolutional neural network (CNN), further preferably
any or all of: a U-Net, V-net, and/or any suitable feed-forward
deep CNN (e.g., using three-dimensional convolutions,
two-dimensional convolutions, etc.), but can additionally or
alternatively be performed using any suitable models, algorithms,
and/or or processes.
[0122] In some variations, for instance, S220 includes a
segmentation process in the event of a suspected ischemia, wherein
the segmentation process segments regions associated with early
ischemic changes through a deep CNN, wherein the deep CNN was
trained based on hand annotated training data. The segmentation
process is preferably performed on a set of one or more images
(e.g., slices, resized slices, etc.) wherein mirror images (e.g.,
overlaid registered images) are received at the deep CNN as an
input, but can additionally or alternatively be performed on the
overlaid image (e.g., registered overlaid image), and/or any other
suitable image(s). In a first specific example, the segmentation
process is performed on the set of image slices, wherein the
segmentation process produces a segmented region corresponding to a
suspected ischemic region based on analysis of the set of images
and an overlaid set of images with the set of images and its mirror
images (e.g., based on a ratio of voxels in the segmented region
relative a mirror image exceeding a threshold).
[0123] The segmentation process can optionally include assigning
one or more scores, such as a probability score (e.g., between 0
and 1) to each base unit (e.g., pixel, voxel, etc.) of the input,
which indicates a likelihood that the base unit represents a part
of the brain condition (e.g., ischemic core) or set of brain
conditions being tested for. A segmentation region (e.g., initial
segmentation region, final segmentation region, etc.) is then
formed from the base units having a probability score above a
predetermined threshold.
[0124] In one variation, the segmentation of a set of NCCT scan
slices functions to segment regions which are consistent with an
ischemic conditions. In a specific example, a 3D array is formed
from the set of scan slices, the 3D array containing a set of
probability values between 0 and 1 for each of the set of voxels in
a 3D representation of the scan slices, the probability values
corresponding to a likelihood that each voxel corresponds to an
ischemic feature (e.g., as described above), such as based on
comparison with a mirror image region. A set of voxels (e.g.,
contiguous voxels, connected voxels, bordering voxels, etc.),
preferably adjacent (e.g., contiguous) voxels (but alternatively
non-adjacent or partially adjacent voxels), having a ratio above a
predetermined threshold in comparison with their mirror image
counterparts represents a region suspected of having an ischemic
condition. The probabilistic output of the network can then be
converted into a binary mask defined as all voxels having a
probability greater than this threshold (e.g., 0.5, 0.7, between
0.4 and 0.8, etc.), which forms the segmentation.
[0125] The probability threshold can optionally be dependent on any
number of features of the segmentation and/or images, such as any
or all of: the location of the segmented region (e.g.,
intraparenchymal, intraventricular, epidural, subdural,
subarachnoid, etc.), the suspected condition (e.g., and a severity
of the condition being tested for), metadata, and/or any other
suitable features. Alternatively, the probability threshold can be
constant.
[0126] Additionally or alternatively, probabilities from multiple
voxels can be aggregated (e.g., averaged) and compared with a
threshold, a minimum number of high probabilities can need to be
reached, and/or any probabilities can be examined in any suitable
ways.
[0127] Further additionally or alternatively, the method can be
performed in absence of determining a probability score
[0128] S220 can optionally include evaluating one or more exclusion
criteria in the image data (e.g., potential affected brain region,
segmented brain region, etc.), which can function to verify that
the image data is relevant for evaluation in the rest of the
method, save time and/or resources by eliminating irrelevant scans,
check for a particular set of false positives (e.g., artifacts,
eliminate one or more categories of false positives while still
maintaining an overall high percentage of false negatives,
minimizing a number of false positives, eliminating one or more
categories of false positives while still maintaining an overall
high percentage of false positives, etc.), route a set of instances
corresponding to one or more exclusion criteria to another workflow
in a healthcare facility, or perform any other suitable function.
In some variations, for instance, a set of exclusion criteria are
evaluated, which function to keep "close call" false positives
(e.g., questionable pathologies, affected brain tissue, etc.) while
eliminating false positives caused by non-physiological events
(e.g., metal artifact, poor image quality, etc.). Additionally or
alternatively, exclusion criteria are evaluated to minimize an
overall number of false positives, thereby minimizing, for
instance, unnecessary interruptions to specialists and/or any other
number of recipients (e.g., clinical trial principal
investigators). Alternatively, the method can partially or fully
process all sets of instances.
[0129] Evaluating exclusion criteria preferably includes checking
for any or all of: the presence of an artifact in one or more of
the set of instances (e.g., metallic artifact, aneurysm clip,
etc.), improper timing at which the set of instances were taken at
an imaging modality (e.g., premature timing, improper timing of a
bolus, etc.), one or more incomplete regions (e.g., features,
anatomical features, etc.) in the set of instances (e.g.,
incomplete skull, incomplete vessel, incomplete soft matter region,
etc.), an incorrect scan type or body part (e.g., non-head CT scan,
non-contrast CT scan, etc.), poor image quality (e.g., blurry
images, low contrast, etc.), movement of the patient during the
scan (e.g., manifesting as bright streaks in one or more images), a
calcification, or any other suitable exclusion criteria.
[0130] In one variation, a set of images (e.g., instances, series,
etc.) are evaluated to determine if an artifact is present, wherein
the set of images is excluded from further processing if an
artifact is found. In a specific example, the method includes
inspecting the HU values of voxels in a soft matter mask, wherein
voxels having a value above a predetermined threshold (e.g., 3000
HU, between 2000 and 4000 HU, etc.) are determined to be a metallic
artifact (e.g., aneurysm clip).
[0131] Checking for exclusion criteria can optionally additionally
or alternatively include comparing a size of a region with a size
criteria, wherein connected components (e.g., segmentations) are
evaluated based on any or all of: area (e.g., number of pixels),
volume (e.g., volume in mL, number of voxels etc.), one or more
characteristic dimensions (e.g., maximum dimension, minimum
dimension, length, width, maximum length, maximum width, thickness,
radius, diameter, etc.), or any other suitable size category. The
size can be compared with a threshold, wherein if the size is below
the threshold (e.g., indicating an artifact, indicating noise,
etc.), the component is eliminated from further processing and/or
consideration. Additionally or alternatively, if the size is above
a threshold (e.g., indicating an image quality issue, indicating an
ischemic core condition too severe for intervention, indicating the
patient moving during the scan, etc.), the component can be
eliminated from further processing and/or consideration; if the
size is within a range of thresholds, the component can be
eliminated from further processing and/or consideration; if the
size is outside a range of thresholds, the component can be
eliminated from further processing and/or consideration; or the
component can be otherwise evaluated and/or further processed
(e.g., evaluated to determine an ischemic core vs. an ischemic
penumbra).
[0132] In some variations, for instance, a size of the segmented
region is compared with a minimum volume threshold, wherein if the
segmented region size is below the minimum volume threshold, the
segmented region is eliminated from further consideration of the
condition, and wherein if the segmented region size is above the
minimum volume threshold, the segmented region continues to further
processing and/or results in the determination of the suspected
condition. Additionally or alternatively, the size threshold can
include a 2D size threshold (e.g., per slice), 1D size threshold
(e.g., largest length of segmentation in a slice, largest width of
segmentation in a slice, thickness, etc.), and/or any other
suitable thresholds.
[0133] Checking for exclusion criteria can optionally further
additionally or alternatively include comparing a location of the
region with a location criteria, wherein connected components
(e.g., segmentations) are evaluated based on their proximity to
another component, such as another anatomical component (e.g.,
bone, skull, etc.), a particular brain region or brain feature
(e.g., particular sulcus, lobe, covering, etc.), an image feature
(e.g., an image edge, etc.), or any other component or general
location. In the event that the relative location (e.g., distance,
proximity, etc.) is below a threshold (indicating that the
component is too close), the component can be discarded.
[0134] The condition typically refers to a hypothesized patient
condition or diagnosis (e.g., ischemic core, ICH, LVO, aneurysm,
stroke, etc.) but can additionally or alternatively include a
severity (e.g., based on a predetermined severity scale), an
urgency, or any other characteristic.
[0135] S220 preferably produces as an output a determination of a
patient condition, wherein this determination can trigger one or
more outcomes (e.g., as described below), prompt further analysis
(e.g., the determination of one or more associated parameters), be
recorded (e.g., in a patient record), and/or be otherwise used. In
variations of ischemic core, for instance, further analysis can be
triggered to determine any or all of: an amount of affected (e.g.,
compromised) brain, an amount of unaffected brain, the particular
affected region (e.g., brain territory, brain lobe, etc.), the
function of the affected region (e.g., motor control, memory,
emotion, etc.), a trajectory of the affected region (e.g., rate of
spreading, blood flow rate, etc.), or any other suitable
parameter.
[0136] In one variation, S220 includes: receiving image data
including a brain image and its mirror image (e.g., individually,
in an overlaid fashion, individually after being registered in an
overlaid fashion, etc.); segmenting a region of the image data
(e.g., in the brain image, in an overlaid image, etc.) indicating a
potential ischemia (e.g., early ischemia) with a CNN (e.g., V-net,
U-net, etc.); and determining the suspected condition based on the
segmentation (e.g., based on a size of the region in comparison
with one or more volume thresholds, based on an aggregated
probability score, based on a binary presence of a segmentation,
etc.). Additionally or alternatively, S220 can include comparing
one or more parameters (e.g., HU value, pixel value, number of
pixels, length, etc.) of the segmented region with its overlapping
region in the mirror image (e.g., corresponding region in the
opposing hemisphere); and determining a potential ischemia based on
this comparison (e.g., exceeds the corresponding parameter by a
predetermined threshold, the corresponding parameter exceeds its
value by a predetermined threshold, etc.). In a specific example,
for instance, S220 includes identifying a relatively dark region in
a left hemisphere of an image; identifying the corresponding region
in the right hemisphere based on the registered overlaid image;
determining a darkness associated with this corresponding region;
comparing the two darknesses and determining that the left
hemisphere region's darkness exceeds the corresponding right
hemisphere region's darkness by a predetermined threshold; and
determining that the patient is potentially experiencing an
ischemic core condition.
4.5 Method--in an Event that the Suspected Condition is Detected,
Determining a Recipient Based on the Suspected Condition S230
[0137] The method includes in an event that the suspected condition
is detected, determining a recipient based on the suspected
condition S230, which functions to facilitate the treatment (e.g.,
triage, acceptance into a clinical trial, etc.) of the patient.
[0138] S230 can additionally, alternatively, and/or equivalently
include determining a treatment option S230, preferably in the
event that a condition is detected (e.g., based on a comparison
with a threshold, based on a binary presence, etc.) but can
additionally or alternatively determine a treatment option when a
condition is not detected, when an analysis is inconclusive, or in
any suitable scenario. S230 can function to match the patient with
a specialist, initiate the transfer of a patient to a 2.sup.nd
point of care (e.g., specialist facility), initiate the transfer of
a specialist to a 1.sup.st point of care, or initiate treatment of
a patient (e.g., surgery, stent placement, mechanical thrombectomy,
etc.) within the 1.sup.st point of care, initiate the matching of a
patient to a clinical trial, or perform any other suitable
function. In some variations, the treatment option is a 2.sup.nd
point of care, wherein it is determined (e.g., suggested, assigned,
etc.) that the patient should be treated at the 2.sup.nd point of
care. Additionally or alternatively, the treatment option can be a
procedure (e.g., surgical procedure, surgical clipping, mechanical
thrombectomy, placement of an aneurysm coil, placement of a stent,
retrieval of a thrombus, stereotactic radiosurgery, etc.),
treatment (e.g., tissue plasminogen activator (TPA), pain killer,
blood thinner, etc.), recovery plan (e.g., physical therapy, speech
therapy, etc.), or any other suitable treatment.
[0139] The recipient and/or treatment is preferably determined
based on a parameter determined from the data packet (e.g., binary
presence of a condition, comparison of a parameter with a
threshold, etc.), but can additionally or alternatively be
determined based on additional data, such as patient information
(e.g., demographic information, patient history, patient treatment
preferences, etc.), input from one or more individuals (e.g., power
of attorney, attending physician, emergency physician, etc.), a
consensus reached by multiple recipients of a notification (e.g.,
majority of members of a care team, all members of a care team,
etc.), or any other suitable information.
[0140] S230 is preferably at least partially performed with
software operating at the remote computing system (e.g., remote
server) but can additionally or alternatively be performed at a
remote computing system separate from a previous remote computing
system, a local computing system (e.g., local server, virtual
machine coupled to healthcare facility server, computing system
connected to a PACS server), or at any other location.
[0141] S230 is preferably performed after a patient condition has
been determined during the method 200. Additionally or
alternatively, S230 can be performed after a patient condition has
been determined in an alternative workflow (e.g., at the 1.sup.st
point of care, at a radiologist workstation during a standard
radiology workflow, in the case of a false negative, etc.), prior
to or absent the determination of a patient condition (e.g., based
on an input from a healthcare worker at the remote computing
system, when patient is admitted to 1.sup.st point of care, etc.),
multiple times throughout the method (e.g., after a first treatment
option fails, after a first specialist is unresponsive, such as
after a threshold amount of time, such as 30 seconds, 1 minute, 2
minutes, etc.), or at any other time during the method.
[0142] S230 preferably determines a recipient (and/or a treatment
option) with a lookup table located in a database accessible at
remote computing system (e.g., cloud-computing system).
Additionally or alternatively, a lookup table can be stored at a
healthcare facility computing system (e.g., PACS server), in
storage at a user device, or at any other location.
[0143] In other variations, the recipient and/or treatment option
can be determined based on one or more algorithms (e.g., predictive
algorithm, trained algorithm, etc.), one or more individuals (e.g.,
specialist, care team, clinical trial coordinator, etc.), a
decision support tool, a decision tree, a set of mappings, a model
(e.g., deep learning model), or through any other process or
tool.
[0144] The lookup table preferably correlates a 2.sup.nd
point-of-care (e.g., healthcare facility, hub, physician,
specialist, neuro-interventionist, etc.), further preferably a
specialist or contact (e.g., administrative worker, emergency room
physician, etc.) associated with the 2.sup.nd point of care, with a
patient condition (e.g., presence of an LVO, presence of a
pathology, severity, etc.), but can additionally or alternatively
correlate a treatment option with the patient condition, and/or any
other suitable recipient (e.g., at a 1.sup.st point of care,
associated with a clinical trial, etc.) with the condition. The
lookup table can further additionally or alternatively correlate a
treatment option with supplementary information (e.g., patient
history, demographic information, heuristic information, etc.).
[0145] The recipient, equivalently referred to herein as contact,
(e.g., healthcare provider, neuro-interventional specialist,
principal investigator, stroke care team member, clinical trial
enrollment committee, etc.) is preferably a healthcare worker, but
can additionally or alternatively be any individual associated with
the treatment of the patient and/or be associated with any
healthcare facility (e.g., prior healthcare facility of patient,
current healthcare facility, recommended healthcare facility)
related to the patient. The contact is further preferably a
specialist (e.g., neuro-interventional specialist, neurosurgeon,
neurovascular surgeon, general surgeon, cardiac specialist, etc.)
but can additionally or alternatively include an administrative
worker associated with a specialist, multiple points of contact
(e.g., ranked order, group, etc.), or any other suitable individual
or group of individuals. The contact is preferably associated with
a hub facility, wherein the hub facility is determined as an option
for second point of care, but can additionally or alternatively be
associated with a spoke facility (e.g., current facility, future
facility option, etc.), an individual with a relation to the
patient (e.g., family member, employer, friend, acquaintance,
emergency contact, etc.), or any other suitable individual or
entity (e.g., employer, insurance company, etc.). Additionally or
alternatively, the contact can be an individual associated with a
clinical trial (e.g., principal investigator at a 1.sup.st point of
care, principal investigator at a 2.sup.nd point of care,
approval/enrollment committee to approve a patient for a clinical
trial, etc.), and/or any other suitable individual.
[0146] The lookup table is preferably determined based on multiple
types of information, such as, but not limited to: location
information (e.g., location of a 1.sup.st point of care, location
of a 2.sup.nd point of care, distance between points of care,
etc.), temporal information (e.g., time of transit between points
of care, time passed since patient presented at 1.sup.st point of
care, etc.), features of condition (e.g., size of occlusion,
severity of condition, etc.), patient demographics (e.g., age,
general health, history, etc.), specialist information (e.g.,
schedule, on-call times, historic response time, skill level, years
of experience, specialty procedures, historic success or
procedures, etc.), healthcare facility information (e.g., current
number of patients, available beds, available machines, etc.), but
can additionally or alternatively be determined based on a single
type of information or in any other suitable way. Information can
be actual, estimated, predicted, or otherwise determined or
collected.
[0147] In some variations, the method 200 includes transmitting
information (e.g., patient condition, data determined from
analysis, optimal set of instances, series, data packet, etc.) to
the computing system associated with the lookup table.
4.6 Method--Preparing a Data Packet for Transfer S240
[0148] The method 200 can include preparing a data packet for
transfer, which can function to produce a compressed data packet,
partially or fully anonymize a data packet (e.g., to comply with
patient privacy guidelines, to comply with Health Insurance
Portability and Accountability Act (HIPAA) regulations, to comply
with General Data Protection Regulation (GDRP) protocols, etc.),
minimize the time to transfer a data packet, annotate one or more
images, or perform any other suitable function. Additionally or
alternatively, any or all of a data packet previously described can
be transferred.
[0149] The data packet is preferably transferred (e.g., once when
data packet is generated, after a predetermined delay, etc.) to a
contact, further preferably a specialist (e.g., associated with a
2.sup.nd point of care, located at the 1.sup.st point of care,
etc.), but can additionally or alternatively be sent to another
healthcare facility worker (e.g., at 1.sup.st point of care,
radiologist, etc.), an individual (e.g., relative, patient, etc.),
a healthcare facility computing system (e.g., workstation), a
server or database (e.g., PACS server), or to any other suitable
location.
[0150] S240 preferably includes compressing a set of images (e.g.,
series), but can additionally or alternatively leave the set of
images uncompressed, compress a partial set of images (e.g., a
subset depicting the condition), or compress any other part of a
data packet. Compressing the data packet functions to enable the
data packet to be sent to, received at, and viewed on a user
device, such as a mobile device. Compressing the data packet can
include any or all of: removing a particular image region (e.g.,
region corresponding to air, region corresponding to hard matter,
region without contrast dye, irrelevant anatomical region, etc.),
thresholding of voxel values (e.g., all values below a
predetermined threshold are set to a fixed value, all values above
a predetermined threshold are set to a fixed value, all values
below -500 HU are set to -500, all voxel values corresponding to a
particular region are set to a fixed value, all voxels
corresponding to air are set to a predetermined fixed value, etc.),
reducing a size of each image (e.g., scale image size by factor of
0.9, scale image size by factor of 0.7, scale image size by factor
of 0.5, scale image size by a factor between 0.1 and 0.9, reduce
image size by a factor of 4, etc.), or through any other
compression method.
[0151] In one variation, the reduction in size of a set of images
can be determined based on one or more memory constraints of the
receiving device (e.g., user device, mobile device, etc.).
[0152] In some variations, such as those involving a patient
presenting with a brain condition (e.g., ischemic core), the images
taken at an imaging modality (e.g., CT scanner) are compressed by
determining an approximate or exact region in each image
corresponding to air (e.g., based on HU value, based on location,
based on volume, etc.) and setting the air region (e.g., voxels
corresponding to the air region, pixels corresponding to the air
region, etc.) to have a fixed value. Additionally or alternatively,
any non-critical region (e.g., bone, unaffected region, etc.) or
other region can be altered (e.g., set to a fixed value, removed,
etc.) during the compression. In a specific example, for instance,
a set of voxels corresponding to air are set to all have a common
fixed value (e.g., an upper limit value, a lower limit value, a
value between 0 and 1, a predetermined value, etc.).
[0153] In some variations, S240 includes identifying an optimal
visualization to be transmitted (e.g., from a remote computing
system) and received (e.g., at a user device), which functions to
prepare an optimal output for a 2.sup.nd point of care (e.g.,
specialist), reduce the time required to review the data packet,
bring attention to the most relevant image data, or to effect any
other suitable outcome.
[0154] In some variations, this involves a reverse registration
process. In a specific example, for instance, this is done through
maximum intensity projection (MIP), where an optimal range of
instances is determined based on which images contain the largest
percentage of the segmented anatomical region of interest in a MIP
image.
[0155] Additionally or alternatively, S240 can include removing
and/or altering (e.g., encrypting) metadata or any unnecessary,
private, confidential, or sensitive information from the data
packet. In some variations, patient information (e.g.,
patient-identifiable information) is removed from the data packet
in order to comply with regulatory guidelines. In other variations,
all metadata are extracted and removed from the data packet.
[0156] S240 can optionally include annotating one or more images in
the data packet, which can function to draw attention to one or
more features of the images, help a specialist or other recipient
easily and efficiently assess the images, and/or perform any other
suitable functions.
[0157] Annotating the images can optionally include adding (e.g.,
assigning, overlaying, etc.) one or more visual indicators (e.g.,
labels, text, arrows, highlighted or colored regions, measurements,
etc.) to one or more images. The incorporation of the visual
indicators can be determined based on any or all of: the suspected
condition (e.g., type of visual indicators designated for the
condition based on a lookup table), one or more thresholds (e.g.,
size thresholds), features of the suspected condition/pathology
(e.g., ischemic penumbra vs. ischemic core, location of condition
within brain, etc.), preferences (e.g., specialist preferences,
point of care preferences, etc.), guidelines (e.g., patient privacy
guidelines), the results of one or more deep learning models,
and/or any other suitable factors or information. In variations
with visual indicators, a table/key can optionally be provided to
explain the visual indicators, which can include any or all of: a
color key defining what colors correspond to; one or more
parameters associated with an indicated region or feature (e.g.,
volume of core); one or more parameters associated with the
image(s) as a whole (e.g., total volume measured across all
regions, number of regions indicated, etc.); and/or any other
suitable information.
[0158] Images can additionally or alternatively be annotated with
one or more metrics, such as one or more parameters (e.g., size as
described above); scores (e.g., a clinical score, a severity score,
etc.); instructions (e.g., recommended intervention); and/or any
other suitable information.
[0159] Additionally or alternatively to being annotated on an
image, any or all of the annotations can be provided in a separate
notification, such as a message, document, and/or provided in any
other suitable way.
[0160] The annotations are preferably determined automatically
(e.g., at a remote computing system implementing the deep learning
models, at a client application, at a mobile device executing a
client application, etc.), but can additionally or alternatively be
determined manually, verified manually, or otherwise
determined.
[0161] In some variations, for instance, a size (e.g., volume) of
the affected area (e.g., ischemia, ischemic core, etc.) has been
found to be helpful in decision-making for the specialist (e.g.,
determining whether or not to intervene, determining a type of
intervention, etc.) and is indicated to the specialist on one or
more images transmitted to him or her. In specific examples, the
region is indicated by (e.g., highlighted in, outlined in, etc.)
one of a set of colors, wherein the color can indicate any or all
of: a type of ischemia, a calculated size (e.g., volume) of the
affected region, and/or any other suitable information.
[0162] S240 can optionally include prescribing a subset of images
to be viewed by the recipient and/or an order in which images
should be viewed (e.g., the particular image shown first to the
recipient upon opening a client application in response to
receiving a notification, the image shown in the thumbnail of a
notification, the only image or subset of images sent, etc.). This
can include, for instance, selecting the image or images indicating
the suspected condition (e.g., all slices containing the suspected
condition, a single slice containing the suspected condition, the
slice containing the largest cross section of a suspected
condition, the slice containing an important or critical feature of
the suspected condition, etc.) for viewing by the recipient. In
specific examples, the recipient (e.g., specialist) is sent a
notification wherein when the recipient opens the notification on a
device (e.g., mobile device), the image corresponding to the
suspected condition is shown first (and optionally corresponds to a
thumbnail image shown to the recipient in the notification).
[0163] The notification(s) and/or image(s) provided to a recipient
are preferably provided within a threshold time period from the
time in which the patient is imaged (e.g., 15 minutes, between 10
and 15 minutes, 10 minutes, 9 minutes, 8 minutes, 7 minutes, 6
minutes, 5 minutes, 3 minutes, 2 minutes, 1 minute, etc.), but can
additionally or alternatively be provided in another suitable time
frame (e.g., greater than 15 minutes), prior to a next action in
the standard of care (e.g., prior to a decision is made by a
radiologist in a parallel workflow), and/or at any other
time(s).
[0164] In some variations, S240 includes storing a dataset (e.g.,
at a remote server, at a local server, at a PACS server, etc.). In
one example, metadata are extracted from the image data and stored
separately from image data in a relational database. In another
example, any or all of the data packet are stored (e.g.,
temporarily, permanently, etc.) to be used in one or more future
analytics processes, which can function to improve the method,
better match patients with suitable treatment options, or for any
other suitable purpose.
[0165] S240 can optionally include applying a low bandwidth
implementation process, which can function to reduce the time until
a specialist receives a first piece of data or data packet (e.g.,
an incomplete series, incomplete study, single instance, single
image, optimal image, image showing occlusion, etc.), reduce the
processing required to inform a specialist of a potential patient
condition, reduce the amount of data required to be reviewed by a
specialist, reduce the amount of data being transmitted from a
remote computing system to a mobile device, or perform any other
suitable function. The low bandwidth implementation process can
include any or all of: organizing (e.g., chunking) data (e.g.,
chunking a series of images based on anatomical region), reordering
data (e.g., reordering slices in a CT series), transmitting a
portion (e.g., single image, single slice, etc.) of a data packet
(e.g., series, study, set of images, etc.) to a device (e.g., user
device, mobile device, healthcare facility workstation, computer,
etc.), sending the rest of the data packet (e.g., only in response
to a request, after a predetermined time has passed, once the data
packet has been fully processed, etc.), or any other process. In a
specific example, for instance, the image data (e.g., slices)
received at a remote computing system from a scanner are chunked,
reordered, and a subset of slices (e.g., single slice) is sent to
the device associated with a specialist first (e.g., prior to
sending a remaining set of slices, in absence of sending a
remainder of slices, etc.).
[0166] In some variations, S240 includes implementing a buffering
protocol, which enables a recipient (e.g., specialist) to start
viewing images (e.g., on a mobile device) prior to all of the
images being loaded at the device (e.g., user device, mobile
device, etc.) at which the recipient is viewing images.
Additionally or alternatively, the buffering protocol can include
transmitting images to the recipient in batches, annotating images
in batches, or otherwise implementing a buffering protocol.
[0167] Additionally or alternatively, S240 can include any other
suitable steps performed in any suitable order.
[0168] The method can additionally or alternatively include any
other suitable sub-steps for preparing the data packet.
[0169] In a first set of variations, S240 includes determining a
subset of one or more images conveying information related to a
suspected patient condition (e.g., showing the presence of the
condition, showing the largest region of the condition, showing a
particular feature of the condition, etc.); optionally annotating
the images (e.g., to point out the condition); and preparing a
notification to be sent to the specialist, wherein the notification
instructs the specialist to view at least the subset of images and
optionally includes a thumbnail depicting one of the set of images
which the specialist can view prior to viewing the images.
4.7 Method--Transmitting Information to a Device Associated with a
Recipient S250
[0170] Transmitting information to a device associated with a
recipient (e.g., at the 1.sup.st point of care, at a 2.sup.nd point
of care, a specialist, etc.) S250 (e.g., as shown in FIG. 6) can
optionally function to initiate a pull from a 1.sup.st point of
care to a 2.sup.nd point of care, which can decrease time to care,
improve quality of care (e.g., better match between patient
condition and specialist), or have any other suitable outcome.
Preferably, the 2.sup.nd point of care is a hub facility (e.g.,
specialist facility, interventional center, comprehensive stroke
center, etc.). In some variations, the 1.sup.st point of care
(e.g., healthcare facility at which patient initially presents)
also functions as the 2.sup.nd point of care, such as when a
suitable specialist is associated with the 1.sup.st point of care,
the 1.sup.st point of care is a hub (e.g., specialist facility,
interventional center, comprehensive stroke center, etc.), it is
not advised to transfer the patient (e.g., condition has high
severity), or for any other reason. Additionally or alternatively,
S250 can function to enroll a patient in a clinical trial, treat
the patient (e.g., at the 1.sup.st point of care, at a 2.sup.nd
point of care, etc.), and/or perform any other suitable
functions.
[0171] S250 is preferably performed after a data packet (e.g.,
compressed data packet, encrypted data packet, etc.) and a
recipient have been determined, but can additionally or alternative
be performed at any or all of: in response to a 2.sup.nd point of
care being determined, multiple times throughout the method (e.g.,
to multiple recipients, with multiple data packets, with updated
information, after a predetermined amount of time has passed since
a notification has been sent to a first choice specialist, etc.),
or at any other time during the method 200.
[0172] The device is preferably a user device, further preferably a
mobile device. Examples of the user device include a tablet,
smartphone, mobile phone, laptop, watch, wearable device (e.g.,
glasses), or any other suitable user device. The user device can
include power storage (e.g., a battery), processing systems (e.g.,
CPU, GPU, memory, etc.), user outputs (e.g., display, speaker,
vibration mechanism, etc.), user inputs (e.g., a keyboard,
touchscreen, microphone, etc.), a location system (e.g., a GPS
system), sensors (e.g., optical sensors, such as light sensors and
cameras, orientation sensors, such as accelerometers, gyroscopes,
and altimeters, audio sensors, such as microphones, etc.), data
communication system (e.g., a WiFi module, BLE, cellular module,
etc.), or any other suitable component.
[0173] The device is preferably associated with (e.g., owned by,
belonging to, accessible by, etc.) a specialist or other individual
associated with the 2.sup.nd point of care, but can additionally or
alternatively be associated with an individual or computing system
at the 1.sup.st point of care, the patient, a clinical trial, or
any other suitable individual or system.
[0174] In one variation, the device is a personal mobile phone of a
specialist. In another variation, the device is a workstation at a
healthcare facility (e.g., first point of care, second point of
care, etc.).
[0175] In some variations, information is sent to multiple members
(e.g., all members) of a care team or clinical trial team, such as
the care team who is treating, may treat the patient, or and/or
will treat the patient. This can enable the care team members to do
any or all of: make a decision together (e.g., transfer decision,
treatment decision, etc.); communicate together (e.g., through the
client application); and/or perform any other function.
[0176] The information preferably includes a data packet, further
preferably the data packet prepared in S240. Additionally or
alternatively, the information can include a subset of a data
packet, the original data packet, any other image data set, or any
other suitable data. The information further preferably includes a
notification, wherein the notification prompts the individual to
review the data packet at the device (e.g., a message reciting
"urgent: please review!"). The notification can optionally include
a thumbnail with a selected image (e.g., image indicating patient
condition), which a recipient can view quickly, such as prior to
the recipient unlocking device to which the notification is sent.
Additionally or alternatively, the notification can prompt the
individual to review data (e.g., original data packet, uncompressed
images, etc.) at a separate device, such as a workstation in a
healthcare facility, a PACS server, or any other location. Further
additionally or alternatively, the notification can include any
suitable information, such as, but not limited to: instructions
(e.g., for treating patient, directions for reaching a healthcare
facility), contact information (e.g., for emergency physician at
first point of care, administrative assistant, etc.), patient
information (e.g., patient history), or any other suitable
information.
[0177] The notification preferably includes an SMS text message but
can additionally or alternatively include a message through a
client application (e.g., as described above, image viewing
application, medical imaging application, etc.), an email message
(e.g., de-identified email), audio notification or message (e.g.,
recording sent to mobile phone), push notification, phone call, a
notification through a medical platform (e.g., PACS, EHR, EMR,
healthcare facility database, etc.), or any other suitable
notification.
[0178] One or more features of a notification can optionally convey
a severity of the patient condition and/or an urgency of receiving
a response from a recipient, which can function to adequately alert
the recipient and properly prioritize care of the patient (e.g.,
relative to other patients). In specific examples, for instance, an
audio cue associated with a notification indicates an urgency of
treating a patient, so that a recipient of the message knows to
immediately review the images and triage the patient.
[0179] The information is preferably sent to the device through a
client application executing on the user device but can
additionally or alternatively be sent through a messaging platform,
web browser, or other platform. In some variations, the information
is sent to all devices (e.g., mobile phone, smart watch, laptop,
tablet, workstation, etc.) associated with the recipient (e.g.,
specialist), such as all devices executing the client application
associated with the recipient, which functions to increase the
immediacy in which the recipient is notified.
[0180] In one variation, S240 and S250 include preparing a
notification to be sent to a device (e.g., user device, mobile
device, etc.) associated with a recipient (e.g., a specialist),
wherein the notification includes a thumbnail (e.g., as shown in
FIG. 10) indicating a selected image (e.g., compressed image
showing a suspected condition), along with a message instructing
the recipient to review the images in a client application, and
optionally the original images at a workstation afterward. In a
first set of specific examples, upon detection that a read receipt
has not been received (e.g., at the remote computing system) in a
predetermined amount of time (e.g., 30 seconds, 1 minute, 2
minutes, between 0 seconds and 2 minutes, 3 minutes, between 2
minutes and 3 minutes, 5 minutes, greater than 5 minutes, less than
10 minutes, etc.), a second notification is transmitted to a second
recipient (e.g., a second specialist). In a second set of specific
examples, sending the notification further triggers and/or enables
communication to be established among multiple members of a care
team (e.g., a stroke team), such as through a messaging component
of the client application, wherein the images can be viewed and
discussed among the care team members. In a third set of specific
examples, a notification is sent to specialist on a mobile device
of the specialist, compressed images are previewed on the
specialist mobile device, and the specialist is notified as being
responsible for viewing non-compressed images on a diagnostic
viewer and engaging in appropriate patient evaluation and relevant
discussion with a treating physician before making care-related
decisions or requests.
[0181] In a second variation, S240 and S250 include preparing a
notification to be sent to a clinical trial research coordinator,
such as a principal investigator, wherein the notification
indicates that the patient is a potential candidate for a clinical
trial (e.g., based on the detection of a suspected condition, based
on a set of clinical trial inclusion criteria, etc.). In specific
examples, a notification can be sent (e.g., automatically,
triggered by the principal investigator, etc.) to a members of a
clinical trial committee (e.g., physician committee), wherein
approval is granted by the committee members (e.g., a majority,
all, at least one, a predetermined number or percentage, etc.),
such as through the client application.
[0182] A notification can optionally be sent which prompts the
individual to provide an input, wherein the input can indicate that
the individual will view, has viewed, or is in the process of
viewing the information (e.g., image data), sees the presence of a
condition (e.g., true positive, serious condition, time-sensitive
condition, etc.), does not see the presence of a condition (e.g.,
false positive, serious condition, time-sensitive condition, etc.),
has accepted treatment of the patient (e.g., swipes right, swipes
up, clicks a check mark, etc.), has denied treatment of the patient
(e.g., swipes left, swipes down, clicks an `x`, etc.), wants to
communicate with another individual (e.g., healthcare worker at
1.sup.st point of care), such as through a messaging platform
(e.g., native to the device, enabled by the client application,
etc.), or any other input. In some variations, one or more
additional notifications are provided to the individual (e.g.,
based on the contents of the input), which can be determined by a
lookup table, operator, individual, decision engine, or other tool.
In one example, for instance, if the individual indicates that the
condition is a true positive, information related to the transfer
of the patient (e.g., estimated time of arrival, directions to the
location of the patient, etc.) can be provided (e.g., in a transfer
request, wherein patient transfer to a specified location, such as
the 2.sup.nd point of care, can be initiated upon transfer request
receipt). In some variants, the data (e.g., images) are displayed
on the user device (e.g., mobile device, workstation) in response
to user interaction with the notification (e.g., in response to
input receipt). However, the input can trigger any suitable action
or be otherwise used.
[0183] Additionally or alternatively, an input can automatically be
received from the client application, such as a read receipt when
the individual has opened the data packet, viewed the notification,
or interacted with the client application in any other suitable
way. In one example, if a read receipt is not received (e.g., at
the remote computing system) from the device within a predetermined
amount of time (e.g., 10 seconds), a second notification and/or
data packet (e.g., compressed set of images) are sent to a second
individual (e.g., second choice specialist based on a lookup
table).
[0184] In some variations, various outputs can be sent from the
client application (e.g., at the user device) to one or more
recipients (e.g., to a second user device, client application on a
work station, on a computing system, etc.), such as recipients
associated with a first point of care (e.g., radiologists,
emergency physicians, etc.). The outputs can be determined based on
the inputs received at the client application associated with the
individual (e.g., acceptance of case, verification of true
positive, etc.), based on a lookup table, or otherwise determined.
The outputs preferably do not alter the standard radiology workflow
(e.g., are not shared with radiologists; radiologists are not
notified), which functions to ensure that the method 200 is a true
parallel process, and that the standard radiology workflow results
in an independent assessment of the patient, but can additionally
or alternatively cut short a workflow, bring a specialist in on the
patient case earlier than normal, or affect any other process in a
healthcare facility.
[0185] The method can additionally or alternatively include
initiating the transfer of a patient, wherein the transfer includes
a recommendation that the patient be considered for a clinical
and/or research trial, based on one or more of: a suspected
clinical condition of the patient (e.g., ischemic core), patient
information (e.g., demographic information), a patient's
willingness or potential willingness to participate, and/or any
other suitable information. Initiating the recommendation can
include transmitting any or all of the notifications described
above (e.g., text message, call, email, etc.) to a specialist
involved in the clinical and/or research trial, a specialist who
has actively turned on notifications for clinical trial
recruitment, a researcher, a research principal investigator, an
administrative assistant, the patient himself, or any other
suitable entity or individual.
4.8 Method--Receiving an Input from the Recipient
[0186] The method 200 can include receiving an input from the
recipient, which functions to determine a next step for the
patient, and can include any or all of: a confirmation of the
suspected condition; a rejection of the suspected condition (e.g.,
false positive); an acceptance by a specialist and/or care team
(e.g., stroke team) to treat the patient (e.g., at a 1.sup.st point
of care, at a 2.sup.nd point of care, etc.); a rejection of a
specialist and/or care team to treat the patient; a read receipt
and/or an indication of a lack or a read receipt within a
predetermined time threshold; an approval to enroll the patient in
a clinical trial; and/or any other suitable input.
[0187] In some variations, a notification is sent in S250 which
prompts the individual to provide an input, wherein the input can
indicate that the individual will view, has viewed, or is in the
process of viewing the information (e.g., image data), sees the
presence of a condition (e.g., true positive, serious condition,
time-sensitive condition, etc.), does not see the presence of a
condition (e.g., false positive, serious condition, time-sensitive
condition, etc.), has accepted treatment of the patient (e.g.,
swipes right, swipes up, clicks a check mark, etc.), has denied
treatment of the patient (e.g., swipes left, swipes down, clicks an
`x`, etc.), wants to communicate with another individual (e.g.,
healthcare worker at 1.sup.st point of care), such as through a
messaging platform (e.g., native to the device, enabled by the
client application, etc.), or any other input. In some variations,
one or more additional notifications are provided to the individual
(e.g., based on the contents of the input), which can be determined
by a lookup table, operator, individual, decision engine, or other
tool. In one example, for instance, if the individual indicates
that the condition is a true positive, information related to the
transfer of the patient (e.g., estimated time of arrival,
directions to the location of the patient, etc.) can be provided
(e.g., in a transfer request, wherein patient transfer to a
specified location, such as the 2.sup.nd point of care, can be
initiated upon transfer request receipt). In some variants, the
data (e.g., images) are displayed on the user device (e.g., mobile
device, workstation) in response to user interaction with the
notification (e.g., in response to input receipt). However, the
input can trigger any suitable action or be otherwise used.
[0188] Additionally or alternatively, an input can automatically be
received from the client application, such as a read receipt when
the individual has opened the data packet, viewed the notification,
or interacted with the client application in any other suitable
way. In one example, if a read receipt is not received (e.g., at
the remote computing system) from the device within a predetermined
amount of time (e.g., 10 seconds), a second notification and/or
data packet (e.g., compressed set of images) are sent to a second
individual (e.g., second choice specialist based on a lookup
table).
[0189] In some variations, various outputs can be sent from the
client application (e.g., at the user device) to one or more
recipients (e.g., to a second user device, client application on a
work station, on a computing system, etc.), such as recipients
associated with a first point of care (e.g., radiologists,
emergency physicians, etc.). The outputs can be determined based on
the inputs received at the client application associated with the
individual (e.g., acceptance of case, verification of true
positive, etc.), based on a lookup table, or otherwise determined.
The outputs preferably do not alter the standard radiology workflow
(e.g., are not shared with radiologists; radiologists are not
notified), which functions to ensure that the method 200 is a true
parallel process, and that the standard radiology workflow results
in an independent assessment of the patient, but can additionally
or alternatively cut short a workflow, bring a specialist in on the
patient case earlier than normal, or affect any other process in a
healthcare facility.
[0190] The outputs can include any or all of: the suspected
condition, parameters (e.g., volume of an ischemic core) and/or
scores (e.g., severity score, urgency score, etc.) associated with
the suspected condition; the selection of one or more recipients of
a notification (e.g., established and/or proposed care team of the
patient); a proposed and/or confirmed intervention for the patient
(e.g., type of procedure); an updated status (e.g., location,
health status, intervention status, etc.) of one or more patients
(e.g., a centralized list of all patients being reviewed by and/or
treated by a specialist; a consent of the patient (e.g., for a
clinical trial); an estimated parameter of the patient (e.g.,
estimated time of arrival at a second point of care); and/or any
other suitable outputs.
4.9 Method--Initiating Treatment of the Patient
[0191] The method can additionally or alternatively include
initiating treatment (e.g., transfer) of the patient, wherein the
treatment can include any or all of the treatment options described
above, such as ay or all of: a point of care (e.g., remain at
1.sup.st point of care, be transferred to a 2.sup.nd point of care,
etc.) at which the patient will be treated; a procedure to treat
the suspected condition; a specialist and/or care team to be
assigned to the patient; a clinical trial in which to enroll the
patient; and/or any other suitable treatments.
[0192] In variations involving recommending the patient for a
clinical trial, initiating treatment of the patient can include
receiving a recommendation that the patient be considered for a
clinical and/or research trial, based on one or more of: a
suspected clinical condition of the patient (e.g., ischemic core,
ischemic penumbra, etc.), patient information (e.g., demographic
information), a patient's willingness or potential willingness to
participate, and/or any other suitable information. Initiating the
recommendation can include transmitting any or all of the
notifications described above (e.g., text message, call, email,
etc.) to a specialist involved in the clinical and/or research
trial, a specialist who has actively turned on notifications for
clinical trial recruitment, a researcher, a research principal
investigator, an administrative assistant, the patient himself, or
any other suitable entity or individual.
4.8 Method--Aggregating Data S260
[0193] The method 200 can optionally include any number of
sub-steps involving the aggregation of data involved in and/or
generated during the method 200, which can function to improve
future iterations of the method 200 (e.g., better match patients
with a specialist, decrease time to treat a patient, increase
sensitivity, increase specificity, etc.). The aggregated data is
preferably used in one or more analytics steps (e.g., to refine a
treatment option, make a recommendation for a drug or procedure,
etc.), but can additionally or alternatively be used for any other
suitable purpose.
[0194] In some variations, for instance the outcomes of the
patients examined during the method 200 are recorded and correlated
with their corresponding data packets, which can be used to assess
the success of the particular treatment options chosen and better
inform treatment options in future cases.
5. Variations
[0195] In one variation of the system 100, the system includes a
router 110, which operates at a computing system at a 1.sup.st
point of care and receives image data from an imaging modality. The
router transmits the image data to a remote computing system,
wherein a series of algorithms (e.g., machine learning algorithms)
are performed at the remote computing system, which determines a
hypothesis for whether or not a suspected condition (e.g., ICH) is
present based on the image data and/or any associated metadata.
Based on the determination, a contact is determined from a lookup
table (e.g., in storage at the remote computing system), wherein
the contact is notified at a user device (e.g., personal device)
and sent image data through a client application executing on the
user device. One or more inputs from the contact at the application
can be received at the remote computing system, which can be used
to determine a treatment option (e.g., next point of care) for the
patient. However, the system and/or components thereof can be used
in any other suitable manner.
[0196] In a first variation of the method 200, the method operates
in parallel with a standard radiology workflow, which can include
any or all of: at a remote computing system (e.g., remote from the
first point of care), receiving a set of images (e.g., of a brain
of the patient), wherein the set of images is concurrently sent to
the standard radiology workflow operating in parallel with the
method and automatically detecting a condition (e.g., ischemic
core, early signs of ischemic core, etc.) from the set of images.
Upon condition detection, the method can include any or all of,
automatically: determining a second specialist from the standard
radiology workflow, wherein the specialist is associated with a
second point of care; notifying the second specialist on a mobile
device associated with the second specialist before the radiologist
notifies the first specialist; displaying a compressed version of
the set of images on the mobile device; and initiating a pull of
the patient (e.g., from the 1.sup.st point of care to the 2.sup.nd
point of care, from the 1.sup.st point of care to a clinical trial
at a later date, as initiated by a specialist at the 2.sup.nd point
of care, as initiated by a specialist at the 1.sup.st point of
care, etc.).
[0197] In a specific example (e.g., as shown in FIG. 7, as shown in
FIG. 8), the method includes, at a remote computing system,
receiving a set of brain images associated with the patient,
wherein the set of brain images is concurrently sent to a standard
radiology workflow operating in parallel with the method. In the
standard radiology workflow, the radiologist analyzes the set of
brain images and notifies a specialist based on a visual assessment
of the set of brain images at the workstation, wherein the standard
radiology workflow takes a first amount of time. The method can
then include detecting an ischemic condition from the set of brain
images, which includes any or all of: identifying an image dataset
of a head from a brain scan; resizing each of the set of images;
producing a mirror image of each of the set of images;
superimposing the image with its mirror image; registering the
image based on the superimposed image; detecting a potential
ischemic core through a segmentation process using a deep CNN;
providing a notification (e.g., through texting) to a specialist
associated with a 2.sup.nd point of care (e.g., different from the
1.sup.st point of care, within the 1.sup.st point of care, etc.);
sending a compressed image dataset to the specialist; displaying a
high resolution image dataset on a workstation of the specialist;
and triggering an action based on input from the specialist (e.g.,
transfer of patient from the 1.sup.st point of care to the 2.sup.nd
point of care, recommending the patient for a clinical trial,
etc.). Within this variation and/or additionally or alternatively,
the mirror image can be taken as an input after registration to the
original image, wherein the original image is registered (e.g.,
straightened) based on its mirror image, wherein a registered
mirror image of the registered image is then produced and used as
an input.
[0198] In a second variation of the method 200, additional or
alternative to the first, the method includes: at a remote
computing system (e.g., remote from the first point of care),
receiving a set of images (e.g., of a brain of the patient),
automatically detecting a condition (e.g., ischemic core, early
signs of ischemic core, etc.) from the set of images based on a
deep learning model, determining a recipient (e.g., a specialist at
a 1.sup.st point of care, a specialist at a 2.sup.nd point of care,
a research coordinator of a clinical trial, etc.); notifying the
recipient on a mobile device associated with the recipient;
optionally displaying a compressed version of the set of images on
the mobile device; and receiving an input from the recipient.
[0199] In a set of specific examples, the method further includes
establishing communication (e.g., texting, call, HIPAA-compliant
texting, HIPAA-compliant calling, etc.) between recipients, such as
between any or all of: multiple healthcare workers (e.g.,
physicians, surgeons, etc.), multiple research coordinators (e.g.,
from the same clinical trial, from different clinical trials,
etc.), a healthcare worker and a research coordinator (e.g., for
the research coordinator to ask questions from the surgeon, as
shown in FIG. 13, etc.), a research coordinator and a patient
(e.g., to submit a consent form to the patient, to receive a
consent form from the patient, etc.), a healthcare worker and a
patient, and/or between any other suitable users and
individuals.
[0200] In a third variation of the method 200 (e.g., as shown in
FIG. 9, as shown in FIG. 11, as performed in accordance with a
system shown in FIG. 12, etc.), additional or alternative to those
described above, the method functions to evaluate if a patient
presenting with a potential pathology qualifies for a clinical
trial and if so, to alert (e.g., automatically, in a time period
shorter than a determination made by a radiologist in a standard
radiology workflow, etc.) a research coordinator (e.g., principal
investigator) associated with the clinical trial, wherein the
method includes: receiving a data packet comprising a set of images
(e.g., NCCT images of a brain of the patient) sampled at the first
point of care, wherein the data packet is optionally concurrently
sent to the standard radiology workflow; segmenting the images and
comparing with set of clinical trial criteria (e.g., inclusion
criteria, exclusion criteria, etc.); and in an event that the
images satisfy the clinical trial criteria (e.g., according to a
set of thresholds), presenting a notification on a mobile device
associated with the research coordinator (e.g., as shown in FIG.
11). If the research coordinator decides to include the patient in
the clinical trial (e.g., based on the notification, based on a set
of compressed images sent to a user device of the research
coordinator, based on a calculated parameter, based on a consensus
reached by a clinical trial committee in communication with the
research coordinator, etc.), the research coordinator or other user
or entity can optionally transmit a consent form to the patient
(e.g., to a user device of the patient, to a workstation associated
with the 1.sup.st point of care, to a workstation associated with
the 2.sup.nd point of care, etc.) and/or to a healthcare worker
(e.g., to a user device of the healthcare worker, to a workstation
of the healthcare worker, etc.), such as a surgeon, associated with
the patient (e.g., via a client application executing on a user
device of the research coordinator, from a remote computing system,
etc.). Additionally or alternatively, the research coordinator can
communicate (e.g., via a HIPAA-compliant messaging platform
established through the client application, through a text
messaging application, etc.) with a physician associated with the
patient, and/or otherwise review and communicate information.
[0201] Additionally or alternatively, the method can include any or
all of: providing a mobile image viewer at a client application
with visible protected health information after secure login by the
recipient; providing a patient status tracker to keep the recipient
informed of updates to the patient; providing case volume
information, which can detect and alert recipients about patients
throughout the hub and spoke network that can benefit from a
particular treatment (e.g., neurosurgical treatment); providing bed
capacity information to a recipient, which enables increased access
to early surgical intervention (e.g., thereby leading to improved
patient outcomes, decreased hospital length of stay, decreased
ventilator use, etc.); and/or any other processes.
[0202] Additionally or alternatively, the method can include any
other steps performed in any suitable order.
[0203] Although omitted for conciseness, the preferred embodiments
include every combination and permutation of the various system
components and the various method processes, wherein the method
processes can be performed in any suitable order, sequentially or
concurrently.
[0204] As a person skilled in the art will recognize from the
previous detailed description and from the figures and claims,
modifications and changes can be made to the preferred embodiments
of the invention without departing from the scope of this invention
defined in the following claims.
* * * * *