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Radiomics and Machine Learning Prediction of Coronary In-Stent Restenosis Based on DSA Imaging: A Comparative Analysis of Different Strategies

Radiomics and Machine Learning Prediction of Coronary In-Stent Restenosis Based on DSA Imaging: A Comparative Analysis of Different Strategies

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500115474
Enrollment
Unknown
Registered
2025-12-26
Start date
2025-08-16
Completion date
Unknown
Last updated
2026-01-05

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

Conditions

cardiovascular

Interventions

Restenosis Group:None
Non-Restenosis Group:None

Sponsors

Shanghai Pudong New Area Gongli Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
23 Years to 86 Years

Inclusion criteria

Inclusion criteria: Patients with follow-up within one year after coronary stent implantation, possessing complete DSA imaging and clinical data.

Exclusion criteria

Exclusion criteria: Patients with poor DSA image quality, making ROI annotation impossible; Patients with borderline ISR (i.e., 30%-40% luminal stenosis); Patients who do not meet the above inclusion criteria.

Design outcomes

Primary

MeasureTime frame
Incidence of In-Stent Restenosis (ISR);—

Secondary

MeasureTime frame
Prediction performance metrics of machine learning models (Area Under Curve, Accuracy, F1-score, etc.);—

Countries

China

Contacts

Public ContactWang Hairong

Department of Cardiology, Shanghai Gongli Hospital

cwslicer@hotmail.com+86 135 2449 8733

Outcome results

None listed

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