Deep learning algorithms offer new opportunities for precise diagnosis and tailored treatment in coronary artery disease, though generalization, interpretability, and regulatory challenges remain.
Deep learning algorithms offer significant potential to improve the efficiency, objectivity, and precision of diagnosis and treatment in coronary artery disease.
Percutaneous coronary intervention has been a standard treatment strategy for patients with coronary artery disease with continuous ebullient progress in technology and techniques. The application of artificial intelligence and deep learning in particular is currently boosting the development of interventional solutions, improving the efficiency and objectivity of diagnosis and treatment. The ever-growing amount of data and computing power together with cutting-edge algorithms pave the way for the integration of deep learning into clinical practice, which has revolutionized the interventional workflow in imaging processing, interpretation, and navigation. This review discusses the development of deep learning algorithms and their corresponding evaluation metrics together with their clinical applications. Advanced deep learning algorithms create new opportunities for precise diagnosis and tailored treatment with a high degree of automation, reduced radiation, and enhanced risk stratification. Generalization, interpretability, and regulatory issues are remaining challenges that need to be addressed through joint efforts from multidisciplinary community.
Chu et al. (Wed,) conducted a review in Coronary artery disease. Deep learning algorithms was evaluated. Deep learning algorithms offer new opportunities for precise diagnosis and tailored treatment in coronary artery disease, though generalization, interpretability, and regulatory challenges remain.
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