External Validation and Performance of an Artificial Intelligence-Based Quantitative Coronary Angiography Software in a European Cohort

Artificial intelligence-based quantitative coronary angiography (AI-QCA) has recently emerged as a promising tool for real-time lesion assessment in cardiology. We aimed to validate a novel AI-QCA software, trained on a Korean dataset, in a European cohort. We analyzed 556 lesions from 252 subjects in two European datasets. The AI-QCA system performed automated vessel segmentation and measurements of minimum lumen diameter, proximal and distal reference diameters, percent diameter stenosis (%DS) and lesion length. The performance of AI-QCA was assessed using both automated and manual frame selection methods, with all measurements validated against expert manual QCA. AI-QCA achieved a lesion detection rate of 86.2% in automated frame selection. AI-QCA and manual QCA showed strong agreement (Pearson’s r > 0.90, R2 > 0.8 for all QCA measurements). For %DS categorization (