# US 2021/0241454 A1 · Systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking

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- Number: US 2021/0241454 A1
- Document ID: US20210241454A1
- Status: Published application
- Kind: A1
- Application number: 17/214,500
- Filed: 2021-03-26
- Published: 2021-08-05
- 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/US20210241454A1

## 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 of generating a risk assessment of coronary disease of a subject based on image analysis of a non-invasive medical image of the subject and compound quantification analysis of plaque, stenosis, ischemia, and fat parameters derived from the image analysis, the method comprising:
accessing, by a computer system, a medical image of a coronary region of a subject, wherein the medical image of the coronary region of the subject is obtained non-invasively;
identifying, by the computer system utilizing a coronary artery identification algorithm, one or more coronary arteries in the medical image of the coronary region of the subject;
identifying, by the computer system utilizing a plaque identification algorithm, one or more regions of plaque within the one or more coronary arteries identified from the medical image of the coronary region of the subject;
identifying, by the computer system utilizing a fat identification algorithm, one or more regions of fat in the medical image of the coronary region of the subject;
determining, by the computer system, one or more vascular morphology parameters and a set of quantified plaque parameters of the one or more identified regions of plaque based at least in part on analysis of the medical image of the coronary region of the subject, wherein the set of quantified plaque parameters comprises a ratio or function of volume or surface area, heterogeneity index, geometry, and radiodensity of the one or more regions of plaque in the medical image;
quantifying, by the computer system, coronary stenosis or luminal narrowing based at least in part on the one or more vascular morphology parameters and the set of quantified plaque parameters determined from the medical image of the coronary region of the subject; and
determining, by the computer system, a presence or risk of ischemia based at least in part on the one or more vascular morphology parameters and the set of quantified plaque parameters determined from the medical image of the coronary region of the subject;
determining, by the computer system, a set of quantified fat parameters of the one or more identified regions of fat within the medical image of the coronary region of the subject, wherein the set of quantified fat parameters comprises volume, geometry, and radiodensity of the one or more regions of fat in the medical image;
generating, by the computer system, a weighted measure of the determined one or more vascular morphology parameters, set of quantified plaque parameters of the one or more regions of plaque, quantified coronary stenosis, presence or risk of ischemia, and set of quantified fat parameters; and
generating, by the computer system, a risk assessment of coronary disease of the subject based at least in part on the generated weighted measure of the determined one or more vascular morphology parameters, set of quantified plaque parameters of the one or more regions of plaque, quantified coronary stenosis, presence or risk of ischemia, and set of quantified fat parameters,
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 heterogeneity index of the one or more regions of plaque is determined by generating a spatial mapping of radiodensity values across the one or more regions of plaque.

### Claim 3 (depends on 1)

The computer-implemented method of claim 1, wherein the heterogeneity index of the one or more regions of plaque is determined by generating a three-dimensional histogram of radiodensity values across a geometric shape of the one or more regions of plaque.

### Claim 4 (depends on 1)

The computer-implemented method of claim 1, wherein one or more of the coronary artery identification algorithm, plaque identification algorithm, or fat identification algorithm comprises an artificial intelligence or machine learning algorithm.

### Claim 5 (depends on 1)

The computer-implemented method of claim 1, further comprising automatically generating, by the computer system, a Coronary Artery Disease Reporting & Data System (CAD-RADS) classification score of the subject based at least in part on the quantified coronary stenosis.

### Claim 6 (depends on 5)

The computer-implemented method of claim 5, further comprising automatically generating, by the computer system, a CAD-RADS modifier of the subject based at least in part on one or more of the determined one or more vascular morphology parameters, set of quantified plaque parameters of the one or more regions of plaque, quantified coronary stenosis, presence or risk of ischemia, or set of quantified fat parameters, wherein the CAD-RADS modifier comprises one or more of nondiagnostic (N), stent (S), graft (G), or vulnerability (V), wherein N is indicative of a nondiagnostic study, S is indicative of a presence of a stent, G is indicative of a presence of a coronary artery bypass graft, and V is indicative of a presence of vulnerable plaque.

### Claim 7 (depends on 1)

The computer-implemented method of claim 1, wherein one or more of the coronary stenosis or the presence or risk of ischemia is quantified on a vessel-by-vessel basis.

### Claim 8 (depends on 1)

The computer-implemented method of claim 1, wherein the one or more regions of fat comprises one or more of epicardial fat or peri-coronary fat.

### Claim 9 (depends on 1)

The computer-implemented method of claim 1, further comprising generating, by the computer system, a proposed treatment for the subject based at least in part on the generated risk assessment of coronary disease.

### Claim 10 (depends on 1)

The computer-implemented method of claim 1, wherein the medical image comprises a Computed Tomography (CT) image.

### Claim 11 (depends on 10)

The computer-implemented method of claim 10, wherein the medical image comprises a non-contrast CT image.

### Claim 12 (depends on 10)

The computer-implemented method of claim 10, wherein the set of plaque parameters further comprises one or more of a percentage of higher radiodensity calcium plaque or lower radiodensity calcium plaque within the one or more regions of plaque, wherein the higher radiodensity calcium plaque comprises pixels with a Hounsfield radiodensity unit of above 1000, and wherein the lower radiodensity calcium plaque comprises pixels with a Hounsfield radiodensity unit of below 1000.

### Claim 13 (depends on 1)

The computer-implemented method of claim 1, wherein the medical image is obtained using an imaging technique comprising one or more of CT, x-ray, ultrasound, echocardiography, intravascular ultrasound (IVUS), 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 14 (depends on 1)

The computer-implemented method of claim 1, wherein the risk assessment of coronary disease of the subject is further generated based at least in part on comparison of one or more of the determined one or more vascular morphology parameters, set of quantified plaque parameters of the one or more regions of plaque, quantified coronary stenosis, presence or risk of ischemia, or set of quantified fat parameters to a normative reference database.

### Claim 15 (depends on 14)

The computer-implemented method of claim 14, wherein the normative reference database comprises one or more vascular morphology parameters, one or more quantified plaque parameters, quantified coronary stenosis, presence or risk of ischemia, or one or more quantified fat parameters derived from one or more medical images obtained using an imaging technique comprising one or more of CT, x-ray, ultrasound, echocardiography, intravascular ultrasound (IVUS), 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 16 (depends on 1)

The computer-implemented method of claim 1, wherein the set of quantified plaque parameters comprises a percentage composition of plaque comprising different radiodensity values.

### Claim 17 (depends on 1)

The computer-implemented method of claim 1, wherein the set of quantified plaque parameters further comprises diffuseness of the one or more regions of plaque.

### Claim 18 (depends on 1)

The computer-implemented method of claim 1, wherein the set of quantified plaque parameters further comprises a ratio of radiodensity to volume of the one or more regions of plaque.

### Claim 19 (depends on 1)

The computer-implemented method of claim 1, wherein the plaque identification algorithm is configured to determine the one or more regions of plaque by determining a vessel wall and lumen wall of the one or more coronary arteries and identifying a volume between the vessel wall and lumen wall as the one or more regions of plaque.

### Claim 20 (depends on 1)

The computer-implemented method of claim 1, wherein the one or more coronary arteries are identified based at least in part on size of the one or more coronary arteries.

### Claim 21 (depends on 1)

The computer-implemented method of claim 1, wherein the one or more vascular morphology parameters comprises a classification of arterial remodeling.

### Claim 22 (depends on 21)

The computer-implemented method of claim 21, wherein the classification of arterial remodeling comprises positive arterial remodeling, negative arterial remodeling, and intermediate arterial remodeling, and wherein positive arterial remodeling is determined when a ratio of a largest vessel diameter of the one or more regions of plaque to a normal reference vessel diameter is more than 1.1, wherein negative arterial remodeling is determined when the ratio of the largest vessel diameter of the one or more regions of plaque to the normal reference vessel diameter is less than 0.95, and wherein intermediate arterial remodeling is determined when the ratio of the largest vessel diameter of the one or more regions of plaque to the normal reference vessel diameter is between 0.95 and 1.1.

### Claim 23 (depends on 1)

The computer-implemented method of claim 1, wherein the weighted measure is generated by weighting the determined one or more vascular morphology parameters, set of quantified plaque parameters of the one or more regions of plaque, quantified coronary stenosis, presence or risk of ischemia, and set of quantified fat parameters logarithmically or algebraically.

### Claim 24

A system for generating a risk assessment of coronary disease of a subject based on image analysis of a non-invasive medical image of the subject and compound quantification analysis of plaque, stenosis, ischemia, and fat parameters derived from the image analysis, 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:
access a medical image of a coronary region of a subject, wherein the medical image of the coronary region of the subject is obtained non-invasively;
identify, utilizing a coronary artery identification algorithm, one or more coronary arteries in the medical image of the coronary region of the subject;
identify, utilizing a plaque identification algorithm, one or more regions of plaque within the one or more coronary arteries identified from the medical image of the coronary region of the subject;
identify, utilizing a fat identification algorithm, one or more regions of fat in the medical image of the coronary region of the subject;
determine one or more vascular morphology parameters and a set of quantified plaque parameters of the one or more identified regions of plaque based at least in part on analysis of the medical image of the coronary region of the subject, wherein the set of quantified plaque parameters comprises a ratio or function of volume or surface area, heterogeneity index, geometry, and radiodensity of the one or more regions of plaque in the medical image;
quantify coronary stenosis based at least in part on the one or more vascular morphology parameters and the set of quantified plaque parameters determined from the medical image of the coronary region of the subject; and
determine a presence or risk of ischemia based at least in part on the one or more vascular morphology parameters and the set of quantified plaque parameters determined from the medical image of the coronary region of the subject;
determine a set of quantified fat parameters of the one or more identified regions of fat within the medical image of the coronary region of the subject, wherein the set of quantified fat parameters comprises volume, geometry, and radiodensity of the one or more regions of fat in the medical image;
generate a weighted measure of the determined one or more vascular morphology parameters, set of quantified plaque parameters of the one or more regions of plaque, quantified coronary stenosis, presence or risk of ischemia, and set of quantified fat parameters; and
generate a risk assessment of coronary disease of the subject based at least in part on the generated weighted measure of the determined one or more vascular morphology parameters, set of quantified plaque parameters of the one or more regions of plaque, quantified coronary stenosis, presence or risk of ischemia, and set of quantified fat parameters.

### Claim 25 (depends on 24)

The system of claim 24, wherein the heterogeneity index of the one or more regions of plaque is determined by generating a spatial mapping of radiodensity values across the one or more regions of plaque.

### Claim 26 (depends on 24)

The system of claim 24, wherein the heterogeneity index of the one or more regions of plaque is determined by generating a three-dimensional histogram of radiodensity values across a geometric shape of the one or more regions of plaque.

### Claim 27 (depends on 24)

The system of claim 24, wherein one or more of the coronary artery identification algorithm, plaque identification algorithm, or fat identification algorithm comprises an artificial intelligence or machine learning algorithm.

### Claim 28 (depends on 24)

The system of claim 24, wherein the system is further caused to automatically generate a Coronary Artery Disease Reporting & Data System (CAD-RADS) classification score of the subject based at least in part on the quantified coronary stenosis.

### Claim 29 (depends on 28)

The system of claim 28, wherein the system is further caused to automatically generate a CAD-RADS modifier of the subject based at least in part on one or more of the determined one or more vascular morphology parameters, set of quantified plaque parameters of the one or more regions of plaque, quantified coronary stenosis, presence or risk of ischemia, or set of quantified fat parameters, wherein the CAD-RADS modifier comprises one or more of nondiagnostic (N), stent (S), graft (G), or vulnerability (V), wherein N is indicative of a nondiagnostic study, S is indicative of a presence of a stent, G is indicative of a presence of a coronary artery bypass graft, and V is indicative of a presence of vulnerable plaque.

### Claim 30 (depends on 24)

The system of claim 24, wherein the system is further caused to generate a proposed treatment for the subject based at least in part on the generated risk assessment of coronary disease.