Abdulraheem Lubabat Wuraola, Al-Dwa Baraah, Shchekochikhin Dmitry, Gognieva Daria, Chomakhidze Petr, Kuznetsova Natalia, Kopylov Philipp, Bestavashvilli Afina Avtandilovna
World-Class Research Center «Digital Biodesign and Personalized Healthcare», I.M. Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia.
Curr Cardiol Rev. 2025;21(1):e310724232529. doi: 10.2174/011573403X293703240715104503.
Recent endeavors have led to the exploration of Machine Learning (ML) to enhance the detection and accurate diagnosis of heart pathologies. This is due to the growing need to improve efficiency in diagnostics and hasten the process of delivering treatment. Several institutions have actively assessed the possibility of creating algorithms for advancing our understanding of atrial fibrillation (AF), a common form of sustained arrhythmia. This means that artificial intelligence is now being used to analyze electrocardiogram (ECG) data. The data is typically extracted from large patient databases and then subsequently used to train and test the algorithm with the help of neural networks. Machine learning has been used to effectively detect atrial fibrillation with more accuracy than clinical experts, and if applied to clinical practice, it will aid in early diagnosis and management of the condition and thus reduce thromboembolic complications of the disease. In this text, a review of the application of machine learning in the analysis and detection of atrial fibrillation, a comparison of the outcomes (sensitivity, specificity, and accuracy), and the framework and methods of the studies conducted have been presented.
最近的努力促使人们探索机器学习（ML），以加强对心脏疾病的检测和准确诊断。这是因为提高诊断效率和加快治疗过程的需求日益增长。几家机构已经积极评估了创建算法以推进我们对心房颤动（AF）理解的可能性，心房颤动是一种常见的持续性心律失常形式。这意味着现在人工智能正被用于分析心电图（ECG）数据。这些数据通常从大型患者数据库中提取，然后在神经网络的帮助下用于训练和测试算法。机器学习已被用于比临床专家更准确地有效检测心房颤动，如果应用于临床实践，将有助于该病症的早期诊断和管理，从而减少该疾病的血栓栓塞并发症。本文对机器学习在心房颤动分析和检测中的应用、结果（敏感性、特异性和准确性）比较以及所开展研究的框架和方法进行了综述。
