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Automated Echocardiographic Detection of Coronary Artery Disease Using Artificial Intelligence Methods

Automated Echocardiographic Detection of Coronary Artery Disease Using Artificial Intelligence Methods

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT06314295
Enrollment
1500
Registered
2024-03-18
Start date
2024-03-11
Completion date
2026-05-11
Last updated
2025-11-21

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Coronary Artery Disease

Brief 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.

Detailed 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.

Interventions

None listed

Sponsors

Beijing Hospital
Lead SponsorOTHER_GOV

Study design

Observational model
OTHER
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to 90 Years

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

Design outcomes

Primary

MeasureTime frameDescription
Different coronary angiography resultsCoronary angiography examination within 2-3 days after admissionThe degree of coronary artery stenosis

Countries

China

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026