# US 2026/0060643 A1 · Rheumatic heart disease detection from echocardiograms

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- Number: US 2026/0060643 A1
- Document ID: US20260060643A1
- Status: Published application
- Kind: A1
- Application number: 19/108,200
- Filed: 2023-09-15
- Published: 2026-03-05
- Earliest claimed: 2022-09-16
- Assignees: Children's National Medical Center
- Applicants: Children's National Medical Center
- Inventors: Marius George LINGURARU; Pooneh ROSHANITABRIZI; Craig SABLE
- Web page: https://patentgrep.com/patent/US20260060643A1

## CPC

- A61B 8/0883: {for diagnosis of the heart}
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- A61B 8/488: {involving Doppler signals}
- A61B 8/5223: {for extracting a diagnostic or physiological parameter from medical diagnostic data (for algorithms to analyse biomedical images G06T7/0012)}
- A61B 8/5246: {combining images from the same or different imaging techniques, e.g. color Doppler and B-mode}
- A61B 8/5292: {using additional data, e.g. patient information, image labeling, acquisition parameters}
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/048: Activation functions
- G06N 3/09: Supervised learning
- G06N 3/0985: Hyperparameter optimisation; Meta-learning; Learning-to-learn
- G06N 20/10: using kernel methods, e.g. support vector machines [SVM]
- G06N 20/20: Ensemble learning
- G16H 30/40: for processing medical images, e.g. editing
- G16H 40/63: for local operation
- 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
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

A method for detecting rheumatic heart disease (RHD) based on at least an echocardiogram, the method including receiving echocardiogram data, extracting first frames corresponding to at least one echocardiogram view from the echocardiogram data, extracting second frames corresponding to ventricular systole from the first frames corresponding to the at least one echocardiogram view, and determining, via at least one machine learning model, an RHD risk score based on the second frames corresponding to ventricular systole.

## Claims

### Claim 1

A method for detecting rheumatic heart disease (RHD) based on at least an echocardiogram, the method comprising:
receiving, via processing circuitry, echocardiogram data;
extracting, via the processing circuitry, first frames corresponding to at least one echocardiogram view from the echocardiogram data;
extracting, via the processing circuitry, second frames corresponding to ventricular systole from the first frames corresponding to the at least one echocardiogram view; and
determining, via at least one machine learning model executed by the processing circuitry, an RHD risk score based on the second frames corresponding to ventricular systole.

### Claim 2 (depends on 1)

The method of claim 1, wherein the echocardiogram data includes Doppler echocardiogram data, color Doppler echocardiogram data, or B-mode ultrasound data.

### Claim 3 (depends on 1)

The method of claim 1, wherein the at least one echocardiogram view includes an apical 4-chamber (A4CC) view and/or a parasternal long axis (PLAXC) view.

### Claim 4 (depends on 1)

The method of claim 1, wherein determining the RHD risk score via the at least one machine learning model includes assigning greater weight to the second frames corresponding to ventricular systole than to frames corresponding to cardiac phases outside of ventricular systole.

### Claim 5 (depends on 1)

The method of claim 1, further comprising identifying and characterizing a mitral valve regurgitation (MR) jet in the second frames corresponding to ventricular systole and/or an aortic valve regurgitation (AR) jet.

### Claim 6 (depends on 5)

The method of claim 5, wherein the determining the RHD risk score is based on morphological and/or physiological characteristics of the MR jet and/or morphological and/or physiological characteristics of the AR jet.

### Claim 7 (depends on 6)

The method of claim 6, wherein the morphological and/or physiological characteristics of the MR jet include at least one of a size descriptor, a shape descriptor, a ratio between an atrium area and an MR jet size, statistical measures related to MR jet intensity or velocity, and duration of the MR jet.

### Claim 8 (depends on 1)

The method of claim 1, wherein the determining the RHD risk score is based on patient demographic and/or clinical information, information from other valvular heart conditions, and/or image-based information obtained from a deep learning model.

### Claim 9 (depends on 1)

The method of claim 1, further comprising localizing frame data corresponding to at least one atrium region in the second frames corresponding to ventricular systole.

### Claim 10 (depends on 9)

The method of claim 9, wherein determining the RHD score via the at least one machine learning model includes assigning greater weight to the frame data corresponding to at least one atrium region in the second frames than to frame data corresponding to regions outside of the at least one atrium region in the second frames.

### Claim 11 (depends on 1)

The method of claim 1, wherein the at least one machine learning model is an ensemble model including at least one machine learning classifier, and outputs of the at least one machine learning classifier are fused to determine the RHD risk score.

### Claim 12 (depends on 1)

The method of claim 1, further comprising classifying a type of RHD based on the second frames corresponding to ventricular systole.

### Claim 13

A non-transitory computer-readable storage medium for storing computer-readable instructions that, when executed by a computer, cause the computer to perform a method for detecting rheumatic heart disease (RHD) based on at least an echocardiogram, the method comprising:
receiving echocardiogram data;
extracting first frames corresponding to at least one echocardiogram view from the echocardiogram data;
extracting second frames corresponding to ventricular systole from the first frames corresponding to the at least one echocardiogram view; and
determining, via at least one machine learning model, an RHD risk score based on the second frames corresponding to ventricular systole.

### Claim 14 (depends on 13)

The non-transitory computer-readable storage medium of claim 13, wherein the echocardiogram data includes Doppler data, color Doppler echocardiogram data, or B-mode ultrasound data.

### Claim 15 (depends on 13)

The non-transitory computer-readable storage medium of claim 13, wherein the at least one echocardiogram view includes an apical 4-chamber (A4CC) view and/or a parasternal long axis (PLAXC) view.

### Claim 16 (depends on 13)

The non-transitory computer-readable storage medium of claim 13, further comprising identifying and characterizing a mitral valve regurgitation (MR) jet in the second frames corresponding to ventricular systole and/or an aortic valve regurgitation (AR) jet.

### Claim 17 (depends on 16)

The non-transitory computer-readable storage medium of claim 16, wherein the determining the RHD risk score is based on morphological and/or physiological characteristics of the MR jet and/or morphological and/or physiological characteristics of the AR jet.

### Claim 18 (depends on 17)

The non-transitory computer-readable storage medium of claim 17, wherein the morphological and/or physiological characteristics of the MR jet include at least one of a size descriptor, a shape descriptor, a ratio between an atrium area and an MR jet size, statistical measures related to MR jet intensity or velocity, and duration of the MR jet.

### Claim 19 (depends on 13)

The non-transitory computer-readable storage medium of claim 13, further comprising localizing frame data corresponding to at least one atrium region in the second frames corresponding to ventricular systole.

### Claim 20

An apparatus for detecting rheumatic heart disease (RHD) based on at least an echocardiogram, comprising:
processing circuitry configured to
receive echocardiogram data,
extract first frames corresponding to at least one echocardiogram view from the echocardiogram data,
extract second frames corresponding to ventricular systole from the first frames corresponding to the at least one echocardiogram view, and
determine, via at least one machine learning model, an RHD risk score based on the second frames corresponding to ventricular systole.