Hadida Barzilai Dor, Cohen-Shelly Michal, Sorin Vera, Zimlichman Eyal, Massalha Eias, Allison Thomas G, Klang Eyal
Sami Sagol AI Hub, ARC, Sheba Medical Center, 31 Emek Ha'ela, Ramat Gan 5262000, Israel.
Leviev Heart Center, Sheba Medical Center, 31 Emek Ha'ela, Ramat Gan 5262000, Israel.
Eur Heart J Digit Health. 2024 Apr 17;5(4):401-408. doi: 10.1093/ehjdh/ztae027. eCollection 2024 Jul.
Coronary artery disease (CAD) is a leading health challenge worldwide. Exercise stress testing is a foundational non-invasive diagnostic tool. Nonetheless, its variable accuracy prompts the exploration of more reliable methods. Recent advancements in machine learning (ML), including deep learning and natural language processing, have shown potential in refining the interpretation of stress testing data. Adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we conducted a systematic review of ML applications in stress electrocardiogram (ECG) and stress echocardiography for CAD prognosis. Medical Literature Analysis and Retrieval System Online, Web of Science, and the Cochrane Library were used as databases. We analysed the ML models, outcomes, and performance metrics. Overall, seven relevant studies were identified. Machine-learning applications in stress ECGs resulted in sensitivity and specificity improvements. Some models achieved rates of above 96% in both metrics and reduced false positives by up to 21%. In stress echocardiography, ML models demonstrated an increase in diagnostic precision. Some models achieved specificity and sensitivity rates of up to 92.7 and 84.4%, respectively. Natural language processing applications enabled the categorization of stress echocardiography reports, with accuracy rates nearing 98%. Limitations include a small, retrospective study pool and the exclusion of nuclear stress testing, due to its well-documented status. This review indicates the potential of artificial intelligence applications in refining CAD stress testing assessment. Further development for real-world use is warranted.
冠状动脉疾病（CAD）是全球主要的健康挑战。运动负荷试验是一种基础的非侵入性诊断工具。尽管如此，其准确性的差异促使人们探索更可靠的方法。机器学习（ML）的最新进展，包括深度学习和自然语言处理，已显示出在优化负荷试验数据解读方面的潜力。我们遵循系统评价和Meta分析的首选报告项目指南，对ML在心电图（ECG）负荷试验和超声心动图负荷试验中用于CAD预后的应用进行了系统评价。使用医学文献分析和检索系统在线数据库、科学网和考克兰图书馆作为数据库。我们分析了ML模型、结果和性能指标。总体而言，共识别出七项相关研究。ML在ECG负荷试验中的应用提高了敏感性和特异性。一些模型在这两个指标上的准确率均超过96%，并将假阳性率降低了21%。在超声心动图负荷试验中，ML模型的诊断精度有所提高。一些模型的特异性和敏感性分别高达92.7%和84.4%。自然语言处理应用实现了超声心动图负荷试验报告的分类，准确率接近98%。局限性包括研究样本量小且为回顾性研究，以及由于核素负荷试验已有充分记录而将其排除在外。本综述表明人工智能应用在优化CAD负荷试验评估方面的潜力。值得进一步开发以用于实际应用。
