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增强起源预测:深度学习模型用于诊断早发性心室收缩,采用双节律分析,专注于心脏旋转

Kazutaka Nakasone¹, Makoto Nishimori^1,2, Masakazu Shinohara²

¹Division of Cardiovascular Medicine, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-2, Kusunoki-cho, Chuo-ku, Kobe-shi, Hyogo 650-0017, Japan.

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology

|September 13, 2024

概括

一个新的深度学习模型通过分析ECG上的PVC和鼻腔节律 (SR) 准确地预测过早心室收缩 (PVCs) 的起源. 结合SR显著提高了准确性,特别是在反时钟方向旋转的右侧PVC.

科学领域:

心脏病学心脏病学
人工智能在医学中的应用
电心电图是指心电图.

背景情况:

使用12导电心电图 (ECG) 从右侧或左侧区分早心收缩 (PVC) 的起源对于患者管理至关重要.
目前的算法通常仅依赖于PVC形态,但同时存在的鼻节律 (SR) 在提高预测准确性的作用仍然不清楚.

研究的目的:

将双节奏模型 (集成SR和PVC数据) 与仅使用PVC的模型进行比较,以预测下轴PVC的起源.
量化个人心电图线索和节律类型 (SR与PVC) 在预测不同心脏旋转的PVC起源方面的贡献.

主要方法:

采用了混合深度学习方法,结合了基于Resnet50的卷积神经网络和变压器模型.
开发了两个模型:一个PVC模型 (仅PVC数据) 和一个双节奏模型 (PVC和SR数据).
模型的性能在100名来自比利时的患者的外部验证数据集上进行了评估,来自日本和德国的493名患者被用于培训.

主要成果:

双节律模型在外部验证中表现出比PVC模型更好的性能,达到更高的精度 (0.84比0.74),精度 (0.73比0.55),特异性 (0.87比0.68),AUC (0.91比0.86),和F1得分 (0.77比0.68).
PVC形态对预测有77.3%的贡献,而SR对预测有22.7%的贡献.
在患有逆时针心脏旋转的患者中,SR的贡献在预测右侧PVC起源方面尤为重要.

结论:

结合PVC和SR两种形态的深度学习模型显著提高了PVC来源的预测准确性,而不是单纯的PVC分析.
鼻腔节律分析对于准确预测心脏反时钟旋转的个体右侧PVCs的起源尤为重要.

关键词:

算法算法是一种算法.人工智能的人工智能是人工智能.心脏旋转是指心脏的旋转.深度学习是一种深度学习.流出线路的流出线路包括:预先发生的心室收缩.

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