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深度神经网络算法使用心电图检测阻塞性睡眠呼吸暂停

Naima Covassin¹, Kan Liu¹, Jan Bukartyk¹

¹Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.

|September 17, 2025

概括

一个新的人工智能 (AI) -ECG算法有效地使用常规心电图来选阻塞性睡眠呼吸暂停 (OSA). 这种AI-ECG工具对广泛的,低成本的OSA查,特别是对女性的查充满了希望.

科学领域:

心脏病学心脏病学
睡眠医学睡眠医学
人工智能的人工智能

背景情况:

阻塞性睡眠呼吸暂停 (OSA) 非常普遍,但往往被诊断不足.
有效的查工具对于早期发现和管理OSA至关重要.

研究的目的:

开发和评估一种基于机器学习的算法,用于从12导电心电图 (ECG) 中识别OSA.
在大量临床人群中评估AI-ECG模型的诊断性能.

主要方法:

训练了一种深度卷积神经网络模型,从11,299名患者的12导电图数据中检测OSA.
模型的预测性能使用接收器运行特征曲线 (AUC) 下的面积来评估.
评估了整体人口的表现,并按性别分层.

主要成果:

在AI-ECG模型中,在测试样本中检测OSA的AUC达到0.80.
该模型在女性 (AUC:0.82) 与男性 (AUC:0.73) 相比,显示出更高的歧视性表现.
算法的预测能力在各种条件和心电图异常中保持稳健.

结论:

开发的AI-ECG模型显示了作为OSA的非侵入性查工具的巨大潜力.
这项技术可以促进广泛的,低成本的OSA查,改善早期诊断和治疗启动.
该模型在女性中的增强性能需要进一步调查,并突出其潜在的临床实用性.

关键词:

人工智能的人工智能是人工智能.卷积神经网络是一种卷积神经网络.电心电图 (ECG) 是一种心电图.机器学习是机器学习.阻塞性睡眠呼吸暂停症是什么

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