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The application value of artificial intelligence-based peri-coronary fat radiomics and machine learning model construction for coronary plaque burden in diagnosing unstable angina pectoris

The application value of artificial intelligence-based peri-coronary fat radiomics and machine learning model construction for coronary plaque burden in diagnosing unstable angina pectoris

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500108176
Enrollment
Unknown
Registered
2025-08-26
Start date
2025-09-05
Completion date
Unknown
Last updated
2025-09-01

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

Conditions

Unstable angina and stable angina

Interventions

Unstable angina group:None

Sponsors

Beijing Pinggu District Hospital
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: 1.The intracoronary contrast media was well filled, and the image quality met the needs of interpretation; 2.The clinical data are complete.

Exclusion criteria

Exclusion criteria: 1. There are obvious respiratory movement artifacts or cardiac pulsation artifacts in the image; 2. Stent implantation, pacemaker placement or artificial metal valve replacement, coronary artery bypass grafting, etc.; 3. The plaque is located in the myocardial bridge; 4. Myocardial bridge at 10-50mm proximal to RCA and/or 40mm proximal to LAD; 5. Massive pericardial effusion; 6. Congenital malformations of coronary arteries; 7. Previous history of myocardial infarction.

Design outcomes

Primary

MeasureTime frame
Pericoronary fat radiomic features;

Secondary

MeasureTime frame
Coronary plaque burden;High-risk plaques in the coronary arteries;

Countries

China

Contacts

Public ContactFulan Zhang

Beijing Pinggu District Hospital

zhangfulan156@yeah.net+86 10 89992127

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026