Review examines AI techniques for vessel segmentation and stenosis detection in coronary angiography, indicating potential improvements in CAD diagnosis.
Coronary artery disease (CAD) is a leading cause of mortality globally, highlighting the need for accurate diagnostic tools. X-ray-guided coronary angiography is the foundation for assessing CAD, involving three key tasks: key frame selection, vessel segmentation and stenosis detection. This review summarises conventional and advanced artificial intelligence (AI) methods applied to these tasks. AI’s integration is vital for enhancing efficiency and reproducibility, allowing real-time analysis and reducing manual labour. However, challenges remain, including the need for large, annotated datasets and difficulties in implementing AI systems in clinical trials. We discuss these challenges and the limitations of current AI approaches, exploring future directions for AI-driven solutions. Ultimately, we provide insights into AI’s potential to improve the precision and efficiency of coronary angiography analysis and CAD diagnosis, contributing to better clinical outcomes and advancing cardiovascular medicine.
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Chen et al. (2025) studied this question.
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