机器学习用于使用12导电心电图诊断急性冠状动脉综合征:系统性审查
Max Zworth1, Hashim Kareemi2, Suzanne Boroumand3
1Department of Emergency Medicine, University of Ottawa, Ottawa, ON, Canada. mzworth@toh.ca.
CJEM
|September 4, 2023
概括
机器学习 (ML) 模型显示出比临床医生更高的灵敏度,但比临床医生更低的特异性,用于通过心电图 (ECG) 诊断急性冠状动脉综合征 (ACS). 需要进一步的研究来确认MLML.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 在紧急情况下,急性冠状动脉综合征 (ACS) 的及时诊断至关重要.
- 目前的计算机心电图 (ECG) 解释算法存在一些局限性.
- 机器学习 (ML) 模型在临床应用中显示出前景.
研究的目的:
- 系统地审查和比较基于ML的心电图分析对临床医生和非ML软件ACS的诊断性能.
- 在急诊室 (ED) 和医院前环境中评估ML模型.
主要方法:
- 在Medline,Embase,Cochrane Central和CINAHL数据库中进行系统搜索,截至2022年5月18日.
- 包括将ML算法与临床医生或非ML软件进行比较的研究,用于在成人患者中使用12心电图进行ACS诊断.
- 使用QUADAS-2进行偏差风险评估.
主要成果:
- 10项研究符合纳入标准,测试了5种ML模型类型.
- 与临床医生相比,ML模型显示出更高的灵敏度 (0.59-0.98),但较低的特异性 (0.44-0.95) (灵敏度0.22-0.93,特异性0.63-0.98).
- ML模型显示了比临床医生 (AUC 0.67-0.78) 更好的歧视 (AUC 0.79-0.98).
- 在几个研究中发现了显著的异质性和偏差风险.
结论:
- 在ACS诊断中,ML模型为ECG解释提供了更好的区分和灵敏度,但特异性较低.
- 基于ML的心电图分析可以作为安全网,用于识别错过的急性心肌梗塞 (MI).
- 需要更严格的研究来确定ML在心电图解释方面比临床医生优越.
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