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Development and Validation of a SAM-Based Artificial Intelligence Model for Fractional Flow Reserve Assessment from Invasive Coronary Angiography

Development and Validation of a SAM-Based Artificial Intelligence Model for Fractional Flow Reserve Assessment from Invasive Coronary Angiography

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500111230
Enrollment
Unknown
Registered
2025-10-28
Start date
2025-11-01
Completion date
Unknown
Last updated
2025-11-03

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

Conditions

CAD

Interventions

Gold Standard:pressure-wire fractional flow reserve
Index test:Artificial Intelligence derived Fraction Flow Reserve, AI-FFR

Sponsors

Beijing Hospital
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: 1. Age 18–80 years. 2. Coronary angiography showing stenosis of 40–90 % in at least one epicardial vessel.

Exclusion criteria

Exclusion criteria: 1. Inadequate contrast opacification of the target vessel. 2. Severe vessel overlap or excessive tortuosity precluding full visualization of the lesion. 3. Sub-optimal image quality that prevents clear identification of the lumen. 4. Chronic total occlusion (CTO) of the target vessel.

Design outcomes

Primary

MeasureTime frame
Diagnostic accuracy of AI-FFR;—

Secondary

MeasureTime frame
sensitivity;specificity;—

Countries

China

Contacts

Public ContactAi Hu

Beijing Hospital

aihumd@aliyun.com+86 10 8513 2535

Outcome results

None listed

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