Chu Miao, Wu Peng, Li Guanyu, Yang Wei, Gutiérrez-Chico Juan Luis, Tu Shengxian
Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Department of Cardiology, Aarhus University Hospital, Aarhus, Denmark.
JACC Asia. 2023 Feb 15;3(1):1-14. doi: 10.1016/j.jacasi.2022.12.005. eCollection 2023 Feb.
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.
经皮冠状动脉介入治疗一直是冠心病患者的标准治疗策略,其技术和技巧不断蓬勃发展。特别是人工智能和深度学习的应用,目前正在推动介入治疗方案的发展,提高诊断和治疗的效率及客观性。不断增长的数据量、计算能力以及前沿算法为深度学习融入临床实践铺平了道路,这已经彻底改变了成像处理、解读和导航方面的介入工作流程。本综述讨论了深度学习算法的发展及其相应的评估指标以及它们的临床应用。先进的深度学习算法为精确诊断和个性化治疗创造了新机会,具有高度自动化、减少辐射以及增强风险分层的特点。泛化性、可解释性和监管问题仍然是挑战,需要多学科领域共同努力加以解决。