The current state of artificial intelligence based invasive coronary angiography image analysis: a systematic review

Abstract Invasive coronary angiography (ICA) is the reference standard for diagnosing coronary artery disease and guiding percutaneous coronary intervention, yet clinical interpretation relies largely on visual assessment, which is variable and often requires additional invasive testing to assess functional significance. Artificial intelligence (AI)–based analysis of ICA images has emerged as a potential solution to automate interpretation, improve reproducibility and extract anatomical and physiological information directly from angiograms. We conducted a systematic review of AI applications for ICA image analysis, registered in PROSPERO and reported according to PRISMA guidelines. A total of 134 studies were included, covering tasks across the ICA workflow, including automated frame selection, vessel segmentation, lesion detection and quantification, prediction of invasive physiological indices, coronary anatomy labeling, image registration and reconstruction, outcome prediction and left ventricular function estimation. Most studies focused on vessel segmentation and lesion assessment, generally demonstrating high internal performance but marked heterogeneity in datasets, reference standards, evaluation metrics and validation strategies. While earlier work relied predominantly on single-center retrospective validation, more recent studies increasingly incorporate multi-center data, external validation and prospective evaluation. AI-based prediction of invasive physiological indices appears particularly promising for reducing reliance on wire-based measurements, though robust prospective evaluation remains limited. Overall, AI-based ICA analysis has progressed from technical feasibility studies toward clinically oriented applications. However, challenges in generalizability, methodological standardization and workflow integration must be addressed to enable reliable clinical adoption.