Corianò Mattia, Tona Francesco
Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, 35128 Padova, Italy.
Biomedicines. 2022 Mar 10;10(3):639. doi: 10.3390/biomedicines10030639.
Sudden cardiac death (SCD) represents a major challenge in modern medicine. The prevention of SCD orbits on two levels, the general population level and individual level. Much research has been done with the aim to improve risk stratification of SCD, although no radical changes in evidence and in therapeutic strategy have been achieved. Artificial intelligence (AI), and in particular machine learning (ML) models, represent novel technologic tools that promise to improve predictive ability of fatal arrhythmic events. In this review, firstly, we analyzed the electrophysiological basis and the major clues of SCD prevention at population and individual level; secondly, we reviewed the main research where ML models were used for risk stratification in other field of cardiology, suggesting its potentiality in the field of SCD prevention.
心脏性猝死（SCD）是现代医学面临的一项重大挑战。SCD的预防分为两个层面，即普通人群层面和个体层面。尽管在证据和治疗策略方面尚未取得根本性改变，但为改善SCD的风险分层已开展了大量研究。人工智能（AI），尤其是机器学习（ML）模型，是有望提高致命性心律失常事件预测能力的新型技术工具。在本综述中，首先，我们分析了SCD预防在人群和个体层面的电生理基础及主要线索；其次，我们回顾了将ML模型用于心脏病学其他领域风险分层的主要研究，提示其在SCD预防领域的潜力。
