Lots of interesting abstracts and cases were submitted for TCTAP 2026. Below are the accepted ones after a thorough review by our official reviewers. Don’t miss the opportunity to expand your knowledge!
ABS20251017_0002
Artificial Intelligence-Driven Quantitative Coronary Angiography (AI-QCA) to Predict Post-PCI Outcomes: An Iraqi Pilot Study
By Hashim Manea
Presenter
Hashim Manea
Authors
Hashim Manea1
Affiliation
Oregon Health and Science University, USA1
View Study Report
ABS20251017_0002
Digital Health and Artificial Intelligence
Artificial Intelligence-Driven Quantitative Coronary Angiography (AI-QCA) to Predict Post-PCI Outcomes: An Iraqi Pilot Study
Hashim Manea1
Oregon Health and Science University, USA1
Background
Manual quantitative coronary angiography (QCA) is time-consuming and prone to inter-observer variation. Artificial intelligence (AI) may enhance accuracy and outcome prediction after PCI.
Methods
A prospective study at Najaf Cardiac Center (2023–2025) involving 210 PCI patients. Angiograms were analyzed by AI-QCA software (Medis Suite v3.2, Netherlands) and compared with manual QCA for diameter stenosis (DS%) and lesion length. Predictive modeling used logistic regression to associate AI-QCA parameters with 12-month MACE.
Results
AI-QCA analysis time was < 20 s vs manual > 5 min. Correlation between AI and manual DS% = r = 0.94 (p < 0.001). AI-derived residual DS% > 25 % independently predicted MACE (OR 2.8, 95 % CI 1.3–5.9, p = 0.008). ROC AUC = 0.87 for AI vs 0.74 for manual QCA.
Conclusion
AI-QCA provides rapid, reliable lesion assessment and prognostic value for post-PCI events. Implementation in Iraqi catheterization labs can standardize PCI evaluation and improve decision-making.