在LBBB中使用人工智能进行心电图查:评估模型开发和转移学习方法
Hak Seung Lee1, Sooyeon Lee2, Sora Kang1
1Digital Healthcare Institute, Sejong Medical Research Institute, Bucheon, Republic of Korea; Medical AI Co, Ltd, Seoul, Republic of Korea.
JACC. Advances
|August 22, 2025
概括
一种通用人工智能 (AI) - ECG模型有效地选左心室缩功能障碍 (LVSD) 的患者. 在不需要精心策划的LBBB数据的情况下,转移学习可以提供一个小的性能提升.
科学领域:
- 心脏病学
- 人工智能
- 医疗诊断
背景情况:
- 左束枝阻塞 (LBBB) 是一种常见的心电图发现,与左心室缩功能障碍 (LVSD) 相关.
- AI-ECG分析显示了LVSD查的潜力,但对LBBB患者的最佳模型类型尚不清楚.
研究的目的:
- 评估四种AI-ECG模型用于在LBBB患者中检测LVSD.
- 评估不同训练数据策略对模型性能的影响.
主要方法:
- 开发了四种AI-ECG模型:一般,LBBB特定 (自动化/专家) 和转移学习.
- 在超过364,000张心电图上接受培训;在1334张LBBB患者心电图上进行外部验证.
- 通过AUROC,灵敏度和特异性来评估性能.
主要成果:
- 转移学习 (AUROC 0.903) 和一般AI-ECG模型 (AUROC 0.899) 显示了可比的高性能.
- 特定于LBBB的模型具有较低的AUROC (0.879,0.841).
- 一般模型在灵敏度上表现出色;转移学习模型在特异性上.
结论:
- 广泛的AI-ECG模型可用于在LBBB患者中检测LVSD.
- 在没有专门的LBBB数据集的情况下,转移学习提供了边际改进.
- 在不同的临床群体中验证对AI-ECG工具至关重要.
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