# US 10,373,315 B2 · Method and system for computer-aided triage > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,373,315 B2 - Document ID: US10373315B2 - Status: Granted - Kind: B2 - Application number: 16/012,458 - Filed: 2018-06-19 - Published: 2018-12-20 - Granted: 2019-08-06 - Earliest claimed: 2017-06-19 - Assignees: Viz.ai Inc. - Applicants: Viz.ai, Inc. - Inventors: Christopher Mansi; David Golan - Web page: https://patentgrep.com/patent/US10373315B2 ## CPC - G06T 7/0012: {Biomedical image inspection} - A61B 5/4064: {Evaluating the brain (for intracranial pressure A61B5/031; for cerebral blood gases A61B5/14553; using EEG A61B5/369)} - G06T 7/11: Region-based segmentation - G16H 30/20: for handling medical images, e.g. DICOM, HL7 or PACS - G16H 30/40: for processing medical images, e.g. editing - G16H 40/20: for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - G16H 80/00: ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring - H04L 67/12: specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks - A61B 5/002: {Monitoring the patient using a local or closed circuit, e.g. in a room or building (A61B5/0017 takes precedence)} - A61B 5/02007: {Evaluating blood vessel condition, e.g. elasticity, compliance} - A61B 5/7264: {Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems} - G06T 2207/10016: Video; Image sequence - G06T 2207/10081: Computed x-ray tomography [CT] - G06T 2207/10088: Magnetic resonance imaging [MRI] - G06T 2207/20072: Graph-based image processing - G06T 2207/20076: Probabilistic image processing - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/30016: Brain - G06T 2207/30101: Blood vessel; Artery; Vein; Vascular - G06T 2207/30172: Centreline of tubular or elongated structure - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## Abstract A system for computer-aided triage can include a router, a remote computing system, and a client application. A method for computer-aided triage can include determining a parameter associated with a data packet, determining a treatment option based on the parameter, and transmitting information to a device associated with a second point of care. ## Claims ### Claim 1 A method for computer-aided triage, the method comprising, at a remote computing system remote from a first point of care: receiving, at the remote computing system, a set of Digital Imaging and Communications in Medicine (DICOM) brain images associated with a patient, wherein the set of DICOM brain images is concurrently sent to a standard radiology workflow operating in parallel with the method, wherein, in the standard radiology workflow, a radiologist analyzes the set of DICOM brain images and notifies a specialist based on a visual assessment of the set of DICOM brain images at a workstation, wherein the standard radiology workflow takes a first amount of time; at the remote computing system, automatically detecting a potential cerebral artery occlusion from the set of DICOM brain images, comprising: segmenting a large vessel region from the set of DICOM brain images; extracting a centerline from the large vessel region; calculating a centerline length of the large vessel region based on the centerline, wherein calculating the centerline length comprises a centerline extension process; comparing the centerline length with a predetermined threshold; and detecting the potential cerebral artery occlusion when the centerline length is less than the predetermined threshold; upon the potential cerebral artery occlusion detection, automatically: determining, at the remote computing system, the specialist from the standard radiology workflow, wherein the specialist is associated with a second point of care; notifying the specialist on a mobile device associated with the specialist, wherein the specialist is notified in a second amount of time shorter than the first amount of time, wherein the radiologist is not automatically notified upon potential cerebral artery occlusion detection; displaying a compressed version of the set of DICOM brain images on the mobile device; and displaying a high-resolution version of the set of DICOM brain images on a workstation associated with the specialist. ### Claim 2 (depends on 1) The method of claim 1, wherein the predetermined threshold is greater than an upper limit of a standard combined anatomical length of a large vessel associated with the large vessel region. ### Claim 3 (depends on 1) The method of claim 1, wherein the second amount of time is less than 8 minutes. ### Claim 4 (depends on 1) The method of claim 1, wherein processing the data packet is initiated after a predetermined time period has passed after receiving the data packet at the remote computing system. ### Claim 5 (depends on 1) The method of claim 1, further comprising: receiving a transfer request at the workstation associated with the specialist, the transfer request associated with the set of DICOM brain images; and initiating a transfer of the patient from the first point of care to the second point of care upon transfer request receipt. ### Claim 6 (depends on 1) The method of claim 1, wherein notifying the specialist comprises presenting a notification on the mobile device, the method further comprising, prior to displaying the compressed version of the set of DICOM brain images: monitoring for an input associated with the notification; displaying the compressed version of the set of images on the mobile device after receipt of the input; and when the input is not received within a predetermined time threshold, determining a second specialist and presenting the notification on a second mobile device associated with the second specialist. ### Claim 7 (depends on 6) The method of claim 6, wherein a mobile device application executing on the mobile device presents the notification and displays the compressed version of the set of images, wherein the specialist is logged into the mobile device application through a specialist account; and wherein a workstation application executing on the workstation associated with the specialist displays the high-resolution version of the set of images, wherein the specialist is logged into the workstation application through the specialist account. ### Claim 8 (depends on 1) The method of claim 1, wherein each of the set of DICOM brain images is associated set of metadata, further comprising generating the compressed version of the set of DICOM brain images, comprising: compressing the set of DICOM brain images; and removing patient information from the set of metadata. ### Claim 9 (depends on 1) The method of claim 1, wherein determining the specialist comprises determining the specialist based on a geographic proximity between the second point of care and the first point of care. ### Claim 10 (depends on 9) The method of claim 9, wherein the set of metadata comprise an identifier associated with the first point of care, and wherein the lookup table correlates the specialist with the identifier, and wherein the lookup table is determined based on a location of the first point of care and a location of the specialist. ### Claim 11 A method for augmenting a standard radiology workflow operating in parallel with the method, the method comprising, at a remote computing system remote from a first point of care: receiving, at the remote computing system, a set of images of a brain of a patient, wherein the set of brain images is concurrently sent to the standard radiology workflow operating in parallel with the method, wherein, in the standard radiology workflow, a radiologist analyzes the set of images and notifies a first specialist in response to determining a presence of a large vessel occlusion; at the remote computing system, automatically detecting a potential large vessel occlusion from the set of images, comprising: segmenting a large vessel region from the set of images; extracting a centerline from the large vessel region; calculating a centerline length of the large vessel region based on the centerline, wherein calculating the centerline length comprises a centerline extension process; comparing the centerline length with a predetermined threshold; and detecting the potential large vessel occlusion when the centerline length is less than the predetermined threshold; upon the potential large vessel occlusion detection, automatically: determining, at the remote computing system, a second specialist from the standard radiology workflow, wherein the second 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; and displaying a compressed version of the set of images on the mobile device. ### Claim 12 (depends on 11) The method of claim 11, wherein the predetermined threshold is greater than an upper limit of a standard combined anatomical length of a large vessel associated with the large vessel region. ### Claim 13 (depends on 11) The method of claim 11, wherein the radiologist is not automatically notified upon potential large vessel occlusion detection, wherein the radiologist in the standard radiology workflow notifies the first specialist at a second time after notifying the second specialist on a mobile device. ### Claim 14 (depends on 11) The method of claim 11, further comprising: at a medical routing system connected to a scanner, wherein the scanner comprises a computed tomography (CT) scanner, intercepting the set of images en route to the standard radiology workflow; and transmitting the set of images from the medical routing system to a remote computing system, wherein the processing system is the remote computing system. ### Claim 15 (depends on 11) The method of claim 11, further comprising displaying a high-resolution version of the set of images on a workstation associated with the second specialist. ### Claim 16 (depends on 15) The method of claim 15, further comprising: receiving a transfer request at the workstation, the transfer request associated with the set of images; and initiating a transfer of the patient from the first point of care to the second point of care upon transfer request receipt. ### Claim 17 (depends on 16) The method of claim 16, wherein a mobile device executing on the mobile device presents the notification and displays the compressed version of the set of images, wherein the second specialist is logged into the mobile device application through a specialist account; and wherein a workstation application executing on the workstation displays the high-resolution version of the set of images, wherein the second specialist is logged into the workstation application through the specialist account. ### Claim 18 (depends on 17) The method of claim 17, further comprising: prior to determining the second specialist, notifying a preliminary specialist in response to large vessel occlusion detection; and monitoring a preliminary specialist account, associated with the preliminary specialist, for activity associated with the set of images; wherein the second specialist is determined when no activity in the preliminary specialist account is detected within a predetermined time window.