# US 12,178,627 B2 · Systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,178,627 B2 - Document ID: US12178627B2 - Status: Granted - Kind: B2 - Application number: 18/641,232 - Filed: 2024-04-19 - Published: 2024-09-19 - Granted: 2024-12-31 - Earliest claimed: 2020-01-07 - Assignees: Cleerly, Inc. - Applicants: Cleerly, Inc. - Inventors: James K. Min; James P. Earls; Hugo Miguel Rodrigues Marques - Web page: https://patentgrep.com/patent/US12178627B2 ## CPC - A61B 6/504: {for diagnosis of blood vessels, e.g. by angiography} - A61B 5/0066: {Optical coherence imaging} - A61B 5/0075: {by spectroscopy, i.e. measuring spectra, e.g. Raman spectroscopy, infrared absorption spectroscopy (A61B5/0071 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} - A61B 5/7267: {involving training the classification device} - A61B 5/742: {using visual displays (displays for heart-related electrical signals, e.g. ECG, A61B5/339)} - A61B 5/7475: {User input or interface means, e.g. keyboard, pointing device, joystick} - A61B 6/032: {Transmission computed tomography [CT]} - A61B 6/037: {Emission tomography} - A61B 6/463: {characterised by displaying multiple images or images and diagnostic data on one display} - A61B 6/467: {characterised by special input means} - A61B 6/481: {involving the use of contrast agents} - A61B 6/5205: {involving processing of raw data to produce diagnostic data} - A61B 6/5217: {extracting a diagnostic or physiological parameter from medical diagnostic data} - A61B 6/583: {using calibration phantoms} - A61B 8/0891: {for diagnosis of blood vessels} - A61B 8/12: in body cavities or body tracts, e.g. by using catheters - A61B 8/14: Echotomography - A61B 8/5223: {for extracting a diagnostic or physiological parameter from medical diagnostic data (for algorithms to analyse biomedical images G06T7/0012)} - A61K 49/04: X-ray contrast preparations - G06F 18/10: Pre-processing; Data cleansing - G06F 18/22: Matching criteria, e.g. proximity measures - G06F 18/2413: based on distances to training or reference patterns - G06T 7/0012: {Biomedical image inspection} - G06V 10/20: Image preprocessing - G06V 10/245: {by locating a pattern; Special marks for positioning} - G06V 10/761: {Proximity, similarity or dissimilarity measures} - G06V 10/764: using classification, e.g. of video objects - G06V 10/82: using neural networks - A61B 5/004: {adapted for image acquisition of a particular organ or body part (A61B5/0082 takes precedence; arrangements for optical scanning A61B5/0062)} - A61B 5/1076: {for measuring dimensions inside body cavities, e.g. using catheters (A61B3/1005 takes precedence)} - A61B 8/587: {Calibration phantoms} - G06T 2207/10081: Computed x-ray tomography [CT] - G06T 2207/10088: Magnetic resonance imaging [MRI] - G06T 2207/10101: Optical tomography; Optical coherence tomography [OCT] - G06T 2207/10104: Positron emission tomography [PET] - G06T 2207/10108: Single photon emission computed tomography [SPECT] - G06T 2207/10116: X-ray image - G06T 2207/10132: Ultrasound image - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/20101: Interactive definition of point of interest, landmark or seed - G06T 2207/30048: Heart; Cardiac - G06T 2207/30101: Blood vessel; Artery; Vein; Vascular - G06V 10/247: {by affine transforms, e.g. correction due to perspective effects; Quadrilaterals, e.g. trapezoids} - G06V 2201/03: Recognition of patterns in medical or anatomical images - Y02A 90/10: Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - A61K: PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES (devices or methods specially adapted for bringing pharmaceutical products into particular physical or administering forms A61J3/00; chemical aspects of, or use of materials for deodorisation of air, for disinfection or sterilisation, or for bandages, dressings, absorbent pads or surgical articles A61L; soap compositions C11D) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - Y02A: TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE ## Abstract The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters. ## Claims ### Claim 1 A computer-implemented method for facilitating risk assessment of coronary artery disease (CAD) for a subject based on image analysis of one or more medical images of the subject, the computer-implemented method comprising: accessing, by a computer system, one or more medical images comprising one or more regions of one or more coronary arteries of a subject; performing, by the computer system, a first analysis of the one or more medical images to determine presence of stenosis in the one or more regions of the one or more coronary arteries of the subject; performing, by the computer system, a second analysis of the one or more medical images for determination of atherosclerosis in the one or more regions of the one or more coronary arteries, wherein the second analysis comprises: identifying one or more regions of plaque in the one or more regions of the one or more coronary arteries of the subject; and generating one or more quantified plaque parameters based at least in part on the identified one or more regions of plaque, the one or more quantified plaque parameters comprising one or more of total plaque volume, volume of low attenuation plaque, volume of non-calcified plaque, or volume of calcified plaque; performing, by the computer system, a third analysis of the one or more medical images for determination of ischemia in the one or more regions of the one or more coronary arteries, wherein the third analysis comprises: generating one or more vascular parameters of the one or more regions of the one or more coronary arteries, the one or more vascular parameters comprising one or more of vascular morphology, lumen wall, or vessel wall; and determining a risk of ischemia for the one or more regions of the one or more coronary arteries based at least in part on the generated one or more vascular parameters; receiving, by the computer system, a user request to generate a graphical representation of results of one or more of the first analysis, the second analysis, or the third analysis; and causing, by the computer system, in response to receiving the user request, display of the graphical representation of results of one or more of the first analysis, the second analysis, or the third analysis, wherein the graphical representation is configured to be further used to facilitate risk assessment of CAD for the subject, wherein the graphical representation of results of the second analysis comprises a first graphical user interface comprising a first panel displaying one or more straightened vessel views of at least a portion of a coronary vessel and a second panel displaying a cross-sectional view of the coronary vessel, wherein the first panel and the second panel are configured such that a first selection of a first location in the first panel along the one or more straightened vessel views causes display of the cross-sectional view at the location in the third panel, wherein the cross-sectional view in the second panel comprises color-coding of one or more of low attenuation plaque, non-calcified plaque, or calcified plaque, wherein the computer system comprises a computer processor and an electronic storage medium. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein the graphical representation comprises a cross-sectional view and a multiplanar view of the one or more regions of the one or more coronary arteries. ### Claim 3 (depends on 2) The computer-implemented method of claim 2, wherein the graphical representation further comprises the identified one or more regions of plaque in the one or more regions of the one or more coronary arteries. ### Claim 4 (depends on 3) The computer-implemented method of claim 3, wherein the graphical representation further comprises a three-dimensional artery tree view of the one or more regions of the one or more coronary arteries. ### Claim 5 (depends on 4) The computer-implemented method of claim 4, wherein the cross-sectional view, the multiplanar view, and the three-dimensional artery tree view of the one or more regions of the one or more coronary arteries are configured to be viewed simultaneously. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, wherein the graphical representation further comprises a representation of the stenosis identified in the one or more regions of the one or more coronary arteries. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, wherein the graphical representation comprises a representation of one or more of total plaque volume, volume of low-density non-calcified plaque, volume of non-calcified plaque, or volume of calcified plaque. ### Claim 8 (depends on 1) The computer-implemented method of claim 1, wherein the first analysis comprises identifying a sum of all stenoses present across a length of a vessel. ### Claim 9 (depends on 1) The computer-implemented method of claim 1, wherein the risk of ischemia is further determined based at least in part on the one or more quantified plaque parameters. ### Claim 10 (depends on 1) The computer-implemented method of claim 1, further comprising: accessing a subsequently obtained set of medical images comprising the one or more regions of one or more coronary arteries of a subject; performing subsequent analysis of the subsequently obtained set of medical images for determination of presence of one or more of atherosclerosis or ischemia; and generating a subsequent graphical representation displaying results of the subsequent analysis of the subsequently obtained set of medical images for determination of presence of one or more of atherosclerosis or ischemia. ### Claim 11 (depends on 1) The computer-implemented method of claim 1, wherein the one or more quantified plaque parameters are generated based at least in part on density of the one or more regions of plaque. ### Claim 12 (depends on 11) The computer-implemented method of claim 11, wherein the density comprises material density. ### Claim 13 (depends on 11) The computer-implemented method of claim 11, wherein the density comprises radiodensity. ### Claim 14 (depends on 13) The computer-implemented method of claim 13, wherein low density non-calcified plaque corresponds to one or more regions of plaque comprising one or more pixels with a radiodensity value between about −189 and about 30 Hounsfield units, wherein non-calcified plaque corresponds to one or more regions of plaque comprising one or more pixels with a radiodensity value between about 31 and about 350 Hounsfield units, and wherein calcified plaque corresponds to one or more regions of plaque comprising one or more pixels with a radiodensity value between about 351 and about 2500 Hounsfield units. ### Claim 15 (depends on 1) The computer-implemented method of claim 1, wherein one or more of the first analysis, the second analysis, or the third analysis is performed using a machine learning algorithm. ### Claim 16 (depends on 1) The computer-implemented method of claim 1, wherein the one or more medical images are obtained using coronary computed tomography angiography (CCTA). ### Claim 17 (depends on 1) The computer-implemented method of claim 1, wherein the one or more medical images are obtained using an imaging modality comprising one or more of computed tomography (CT), x-ray, ultrasound, echocardiography, magnetic resonance (MR) imaging, optical coherence tomography (OCT), nuclear medicine imaging, positron-emission tomography (PET), single photon emission computed tomography (SPECT), or near-field infrared spectroscopy (NIRS). ### Claim 18 (depends on 1) The computer-implemented method of claim 1, wherein the straightened view is configured to illustrate a vessel wall and a lumen wall. ### Claim 19 (depends on 18) The computer-implemented method of claim 18, wherein the vessel wall is an outer wall. ### Claim 20 (depends on 1) The computer-implemented method of claim 1, wherein the first analysis, the second analysis, and the third analysis are performed at least in part using a machine learning algorithm, wherein the machine learning algorithm is trained using a trained set of CT images and coronary vessel information identified from the training set of CT images. ### Claim 21 (depends on 1) The computer-implemented method of claim 1, wherein at least one of the first analysis, the second analysis, or the third analysis are performed at least in part using a machine learning algorithm, wherein the machine learning algorithm is trained using a trained set of CT images and coronary vessel information identified from the training set of CT images. ### Claim 22 (depends on 21) The computer-implemented method of claim 21, wherein the coronary vessel information comprises outer wall, lumen wall, and plaque of at least one coronary artery. ### Claim 23 A system for facilitating risk assessment of coronary artery disease (CAD) for a subject based on image analysis of one or more medical images of the subject, the system comprising: one or more computer readable storage devices configured to store a plurality of computer executable instructions; and one or more hardware computer processors in communication with the one or more computer readable storage devices and configured to execute the plurality of computer executable instructions in order to cause the system to: perform a first analysis of the one or more medical images to determine presence of stenosis in one or more regions of one or more coronary arteries of the subject; perform a second analysis of the one or more medical images for determination of atherosclerosis in the one or more regions of the one or more coronary arteries, wherein the second analysis comprises: identifying one or more regions of plaque in the one or more regions of the one or more coronary arteries of the subject; and generating one or more quantified plaque parameters based at least in part on the identified one or more regions of plaque, the one or more quantified plaque parameters comprising one or more of total plaque volume, volume of low density non-calcified low attenuation plaque, volume of non-calcified plaque, or volume of calcified plaque; perform a third analysis of the one or more medical images for determination of ischemia in the one or more regions of the one or more coronary arteries, wherein the third analysis comprises: generating one or more vascular parameters of the one or more regions of the one or more coronary arteries, the one or more vascular parameters comprising one or more of vascular morphology, lumen wall, or vessel wall; and determining a risk of ischemia for the one or more regions of the one or more coronary arteries based at least in part on the generated one or more vascular parameters; receive a request from a user to generate a graphical representation of results of one or more of the first analysis, the second analysis, or the third analysis; and cause, in response to receiving the request from the user, display of the graphical representation of results of one or more of the first analysis, the second analysis, or the third analysis, wherein the graphical representation is configured to be further used to facilitate risk assessment of CAD for the subject, wherein the graphical representation of results of the second analysis comprises a first graphical user interface comprising a first panel displaying one or more straightened vessel views of at least a portion of a coronary vessel and a second panel displaying a cross-sectional view of the coronary vessel, wherein the first panel and the second panel are configured such that a first selection of a first location in the first panel along the one or more straightened vessel views causes display of the cross-sectional view at the location in the third panel, wherein the cross-sectional view in the second panel comprises color-coding of one or more of low attenuation plaque, non-calcified plaque, or calcified plaque. ### Claim 24 (depends on 23) The system of claim 23, wherein the graphical representation comprises a cross-sectional view, a multiplanar view, and a three-dimensional artery view of the one or more regions of the one or more coronary arteries. ### Claim 25 (depends on 23) The system of claim 23, wherein the risk of ischemia is further determined based at least in part on the one or more quantified plaque parameters. ### Claim 26 (depends on 23) The system of claim 23, wherein the straightened view is configured to illustrate a vessel wall and a lumen wall. ### Claim 27 (depends on 26) The system of claim 26, wherein the vessel wall is an outer wall. ### Claim 28 (depends on 23) The system of claim 23, wherein the first analysis, the second analysis, and the third analysis are performed at least in part using a machine learning algorithm, wherein the machine learning algorithm is trained using a trained set of CT images and coronary vessel information identified from the training set of CT images. ### Claim 29 (depends on 23) The system of claim 23, wherein at least one of the first analysis, the second analysis, or the third analysis is performed at least in part using a machine learning algorithm, wherein the machine learning algorithm is trained using a trained set of CT images and coronary vessel information identified from the training set of CT images. ### Claim 30 (depends on 29) The system of claim 29, wherein the coronary vessel information comprises outer wall, lumen wall, and plaque of at least one coronary artery.