Automated Echocardiographic Detection of Coronary Artery Disease Using Artificial Intelligence Methods

The incidence rate and mortality of coronary artery disease are increasing year by year. Exploring non-invasive, accurate, and widely applicable methods to screen and diagnosis is of great significance. New ultrasound techniques, such as non-invasive myocardial work, have been proven to be superior to traditional ultrasound techniques in screening and diagnosis. However, diagnostic analysis based on ultrasound video images is time-consuming and subjective. The progress of artificial intelligence technology in fully automated quantitative evaluation of video images provides the possibility for computer-aided design screening and diagnosis. At present, the application of artificial intelligence in computer-aided design is a cutting-edge issue in the field of cardiovascular disease research. The application of artificial intelligence technology in the construction of computer-aided diagnostic models based on ultrasound video images is still in its early stages.

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Key information

Conditions

Coronary Artery Disease Arterial Occlusive Diseases Arteriosclerosis Cardiovascular Diseases Coronary Disease Heart Diseases Myocardial Ischemia Vascular Diseases

Age range

18 year–90 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Beijing Hospital

Beijing, China

Location status: Recruiting

Location contact

Fang Wang, Dr

CONTACT

[email protected]

About this study

Clarify the value of new cardiac ultrasound techniques indicators in coronary artery disease diagnosis; 2) To achieve classification and detection of cardiac ultrasound sections; Implementing automatic segmentation and recognition of the left ventricular cavity, left ventricular myocardium, and left atrial wall contours through the CLAS model; Using the another model to achieve heart motion tracking and synthesizing velocity vector maps of the heart flow field. 3) Verify and optimize the coronary artery disease fully automated artificial intelligence diagnostic model mentioned above.

Who can participate

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Patients with suspected coronary artery disease
Patients plan to undergo coronary angiography

Exclusion criteria

Patients with aortic valve stenosis
Patients with aortic valve replacement surgery
Patients with hypertrophic cardiomyopathy
Patients with severe heart valve disease
Patients with severe arrhythmia
Patients with severe cardiomyopathy
Patients with severe congenital heart disease
The quality of ultrasound images is poor

Treatment and study plan

Primary outcomes

Different coronary angiography results

Time frame: Coronary angiography examination within 2-3 days after admission

The degree of coronary artery stenosis

Sponsors and collaborators

Lead sponsor

Beijing Hospital

Other Gov

Registry information

Important dates

Study start: 2024
Primary completion: 2026
Study completion: 2026
First posted: Mar 18, 2024
Registry last updated: Nov 21, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

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This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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