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NCT06314295 Clinical Trial

Automated Echocardiographic Detection of Coronary Artery Disease Using Artificial Intelligence Methods

Coronary Artery Disease Clinical Trial

Official title:
Automated Echocardiographic Detection of Coronary Artery Disease Using Artificial Intelligence Methods

Clinical Trial Details — Status: Not yet recruiting

Administrative data
NCT number NCT06314295
Other study ID # BeijingH-WF
Secondary ID
Status Not yet recruiting
Phase
First received
Last updated
Start date March 11, 2024
Est. completion date April 11, 2026

Study information
Verified date March 2024
Source Beijing Hospital
Contact n/a
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

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.

Description:

1) 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.

Recruitment information / eligibility
Status Not yet recruiting
Enrollment 1500
Est. completion date April 11, 2026
Est. primary completion date March 11, 2026
Accepts healthy volunteers
Gender All
Age group 18 Years to 90 Years
Eligibility Inclusion Criteria: Patients with suspected coronary artery disease who plan to undergo coronary angiography Exclusion Criteria: Patients with ?Aortic valve stenosis, aortic valve replacement surgery, hypertrophic cardiomyopathy; ?Severe heart valve disease, arrhythmia, cardiomyopathy, congenital heart disease; ?The quality of ultrasound images is poor.

Study Design

Related Conditions & MeSH terms

Locations
Country Name City State
n/a

Sponsors (1)
Lead Sponsor Collaborator
Beijing Hospital

Outcome
Type Measure Description Time frame Safety issue
Primary Different coronary angiography results The degree of coronary artery stenosis Coronary angiography examination within 2-3 days after admission
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