# US 2024/0197280 A1 · Systems, devices, and methods for non-invasive image-based plaque analysis and risk determination

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- Number: US 2024/0197280 A1
- Document ID: US20240197280A1
- Status: Published application
- Kind: A1
- Application number: 18/592,176
- Filed: 2024-02-29
- Published: 2024-06-20
- Earliest claimed: 2022-11-14
- Assignees: Cleerly, Inc.
- Applicants: Cleerly, Inc.
- Inventors: James K. Min
- Web page: https://patentgrep.com/patent/US20240197280A1

## CPC

- A61B 6/5217: {extracting a diagnostic or physiological parameter from medical diagnostic data}
- A61B 6/032: {Transmission computed tomography [CT]}
- A61B 6/503: {for diagnosis of the heart}
- A61B 6/504: {for diagnosis of blood vessels, e.g. by angiography}
- A61B 6/507: {for determination of haemodynamic parameters, e.g. perfusion CT}
- A61B 6/5229: {combining image data of a patient, e.g. combining a functional image with an anatomical image}
- G06T 7/0012: {Biomedical image inspection}
- G06T 7/11: Region-based segmentation
- G06T 7/62: of area, perimeter, diameter or volume
- G06V 10/22: by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition
- G06V 10/26: Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
- G16H 30/40: for processing medical images, e.g. editing
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16H 50/30: for calculating health indices; for individual health risk assessment
- G16H 50/50: for simulation or modelling of medical disorders
- G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients
- G06T 2207/10081: Computed x-ray tomography [CT]
- G06T 2207/20076: Probabilistic image processing
- G06T 2207/20084: Artificial neural networks [ANN]
- G06T 2207/30048: Heart; Cardiac
- G06T 2207/30104: Vascular flow; Blood flow; Perfusion
- G06V 2201/031: of internal organs
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

Various embodiments described herein relate to systems, devices, and methods for non-invasive image-based plaque analysis and risk determination. In particular, in some embodiments, the systems, devices, and methods described herein are related to analysis of one or more regions of plaque, such as for example coronary plaque, using non-invasively obtained images that can be analyzed using computer vision or machine learning to identify, diagnose, characterize, treat and/or track coronary artery disease.

## Claims

### Claim 1

A computer-implemented method of determining a likelihood of thin-cap fibroatheroma (TCFA) based at least in part on a plurality of variables derived from non-invasive medical image analysis, the method comprising:
accessing, by a computer system, a medical image of a subject, wherein the medical image of the subject is obtained non-invasively;
analyzing, by the computer system, the medical image of the subject to identify one or more arteries;
generating, by the computer system, one or more quantified vascular parameters based at least in part on the identified one or more arteries;
analyzing, by the computer system, the identified one or more arteries to identify one or more regions of plaque based at least in part on density;
generating, by the computer system, one or more quantified plaque parameters based at least in part on the identified one or more regions of plaque;
determining, by the computer system, a likelihood of presence of TCFA for the one or more regions of plaque without direct identification of TCFA from the medical image, wherein the likelihood of presence of TCFA is determined by applying a machine learning algorithm to the one or more quantified vascular parameters and the one or more quantified plaque parameters, wherein the machine learning algorithm is trained by a dataset comprising one or more quantified vascular parameters and one or more quantified plaque parameters derived from a plurality of other medical images with known presence or absence of TCFA for a region of 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 one or more quantified plaque parameters comprises one or more of percent atheroma volume of total plaque, total plaque volume, percent atheroma volume of low-density non-calcified plaque, percent atheroma volume of non-calcified plaque, percent atheroma volume, low-density non-calcified plaque volume, percent atheroma volume of total calcified plaque, non-calcified plaque volume, total calcified plaque volume, percent atheroma volume of total non-calcified plaque, percent atheroma volume of low-density calcified plaque, percent atheroma volume of high-density calcified plaque, total non-calcified plaque volume, low-density calcified plaque volume, percent atheroma volume of medium-density calcified plaque, high-density calcified plaque volume, medium-density calcified plaque volume, number of high-risk plaque regions, number of segments with calcified plaque, number of segments with non-calcified plaque, plaque area, plaque burden, necrotic core percentage, necrotic core volume, fatty fibrous volume, fatty fibrous percentage, dense calcium percentage, low-density calcium percentage, medium-density calcified percentage, high-density calcified percentage, presence of two-feature positive plaques, or number of two-feature positive plaques.

### Claim 3 (depends on 1)

The computer-implemented method of claim 1, wherein the one or more quantified vascular parameters comprises one or more of vessel length, segment length, lesion length, lumen volume, number of chronic total occlusion (CTO), vessel volume, number of stenosis, severity of stenosis, remodeling index, minimum lumen diameter, maximum lumen diameter, mean lumen diameter, stenosis area percentage, stenosis diameter percentage, number of mild stenosis, number of moderate stenosis, number of zero stenosis, number of severe stenosis, presence of high-risk anatomy, presence of positive remodeling, number of severe stenosis excluding CTO, vessel area, lumen area, diameter stenosis percentage, presence of ischemia, number of stents, reference lumen diameter before stenosis, perivascular fat attenuation, or reference lumen diameter after stenosis. The computer-implemented method of claim 1, wherein the likelihood of presence of TCFA is determined on a subject basis.

### Claim 4 (depends on 1)

The computer-implemented method of claim 1, wherein the determined likelihood of presence of TCFA comprises a percentage of likelihood of presence of TCFA.

### Claim 5 (depends on 1)

The computer-implemented method of claim 1, wherein the determined likelihood of presence of TCFA comprises a binary output of likelihood or unlikelihood of presence of TCFA.

### Claim 6 (depends on 1)

The computer-implemented method of claim 1, further comprising determining a risk of artery disease for the subject based at least in part on the determined likelihood of presence of TCFA.

### Claim 7 (depends on 6)

The computer-implemented method of claim 6, wherein the artery disease comprises coronary artery disease (CAD).

### Claim 8 (depends on 6)

The computer-implemented method of claim 6, wherein the artery disease comprises peripheral artery disease (PAD).

### Claim 9 (depends on 6)

The computer-implemented method of claim 6, wherein the risk of artery disease for the subject is determined based at least in part on comparing the determined likelihood of presence of TCFA against dataset comprising varying risks of artery disease and known presence or absence of TCFA derived from a reference population.

### Claim 10 (depends on 6)

The computer-implemented method of claim 6, further comprising determining a proposed treatment for the subject based at least in part on the determined risk of artery disease.

### Claim 11 (depends on 10)

The computer-implemented method of claim 10, wherein the proposed treatment comprises one or more of a lifestyle treatment, interventive treatment, or medication treatment.

### Claim 12 (depends on 1)

The computer-implemented method of claim 1, wherein the one or more regions of plaque comprises a necrotic core and non-calcified plaque.

### Claim 13 (depends on 1)

The computer-implemented method of claim 1, wherein the one or more regions of plaque comprises one or more of low density non-calcified plaque or non-calcified plaque.

### Claim 14 (depends on 1)

The computer-implemented method of claim 1, wherein the one or more arteries comprises one or more coronary arteries.

### Claim 15 (depends on 1)

The computer-implemented method of claim 1, wherein the one or more arteries comprises one or more coronary arteries, carotid arteries, lower extremity arteries, upper extremity arteries, or aorta.

### Claim 16 (depends on 1)

The computer-implemented method of claim 1, wherein the density comprises absolute density.

### Claim 17 (depends on 1)

The computer-implemented method of claim 1, wherein the density comprises radiodensity.

### Claim 18 (depends on 1)

The computer-implemented method of claim 1, wherein the one or more regions of plaque are identified as low density non-calcified plaque when a radiodensity value is between about −189 and about 30 Hounsfield units.

### Claim 19 (depends on 1)

The computer-implemented method of claim 1, wherein the one or more regions of plaque are identified as non-calcified plaque when a radiodensity value is between about 31 and about 350 Hounsfield units.

### Claim 20 (depends on 1)

The computer-implemented method of claim 1, wherein the one or more regions of plaque are identified as calcified plaque when a radiodensity value is between about 351 and 2500 Hounsfield units.