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机器学习在左心室缩检测:系统审查和元分析

Yilin Li¹, Ke Zhao², Jing Wu¹

¹Department of Geriatrics, The Third People's Hospital of Chengdu, 82 Qinglong Street, Qingyang District, Chengdu, Sichuan Province, China, 610031, Chengdu, Sichuan, China, 86 15881707332.

Journal of medical Internet research

|March 3, 2026

概括

机器学习 (ML) 模型显示了检测左心室缩 (LVH) 的潜力,但准确性各不相同. 未来的研究应该优先考虑成像数据,以改善LVH诊断.

科学领域:

心脏病学心脏病学
人工智能的人工智能
医疗信息学医疗信息学

背景情况:

机器学习 (ML) 越来越多地被用于检测左心室缩 (LVH).
现有研究表明,ML模型用于LVH检测的准确性是可变的,受不同变量和算法的影响.
需要有系统的证据来证明各种ML方法如何影响LVH检测的准确性.

研究的目的:

系统地评估ML方法用于LVH检测的诊断准确性.
为心脏病学中开发先进的人工智能工具提供证据.
了解不同数据类型和算法对LVH的ML模型性能的影响.

主要方法:

在PubMed,Embase,Cochrane图书馆和Web of Science进行了系统的文献搜索,截至2025年11月.
预测模型风险偏差评估工具被用于质量评估.
在验证集的诊断2x2表上进行了元分析和子组分析,按ML模型类型和输入数据 (心电图,临床特征,心声) 分层.

主要成果:

分析了25项研究,揭示了基于输入数据和算法的ML模型的性能变化.
基于心电图 (ECG) 的模型显示了0.76的综合灵敏度和0.84.8的特异性.
基于心声图的模型显示灵敏度范围为0.71-0.94和特异性为0.67-0.96;临床特征模型的灵敏度为0.78和特异性为0.71.

结论:

ML模型对LVH检测具有中等的准确性,但证据有限,异质性很高.
关于LVH的ML模型准确性的结论应谨慎解释,因为它具有显著的变异性.
未来的研究应该集中于开发高性能ML模型,利用成像数据进行更可靠的LVH诊断.

关键词:

在这里,我们可以看到AIAIAI.这是一个ECGECGECGECGECG.人工智能的人工智能是人工智能.心血管疾病的风险.深度学习是一种深度学习.超声心电图 (Echocardiography) 是一种心声回声仪.电心电图 (ECG) 是一种心电图.左心室缩症是指左心室缩症.机器学习是机器学习.进行元分析分析.

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