在使用深度学习的心电图像上识别过敏性心肌病变
Veer Sangha1,2, Lovedeep Singh Dhingra1, Arya Aminorroaya1
1Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
Nature cardiovascular research
|July 22, 2025
概括
一种新的深度学习模型可以从心电图 (ECG) 图像中检测高性心肌病 (HCM),从而改善诊断. 这种人工智能工具在各种临床环境中提供可扩展的基于图像的HCM查.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 超性心肌病变 (HCM) 经常被低诊断,影响患者的治疗结果.
- 目前用于HCM检测的深度学习 (DL) 模型通常需要原始心电图 (ECG) 电压数据,限制了医疗点的应用.
研究的目的:
- 开发和验证一种DL模型,能够从标准的12导心电图像中检测HCM,无论其布局如何.
- 评估模型在各种数据集和临床环境中的性能.
主要方法:
- 在耶鲁纽黑文医院 (YNHH) 的66,987名患者的124,553个心电图上训练了一种DL模型,通过心磁共振 (CMR) 或心声回声学证实了HCM.
- 外部验证使用来自MIMIC-IV,阿姆斯特丹大学医学中心 (AUMC) 和英国生物银行 (UKB) 的ECG图像进行,HCM由CMR或诊断代码定义.
- 评估了该模型在不同格式和机构的ECG图像中检测HCM的能力.
主要成果:
- DL模型实现了高性能,接收器操作特征曲线 (AUROC) 下的面积为0.95 (内部测试),0.94 (MIMIC-IV),0.92 (AUMC) 和0.91 (英国).
- 在前/侧导线 (V4和V5) 中,独立于图像布局,确定了关键的歧视性心电图特征.
- 该模型在各种图像格式和验证网站上展示了强大的性能.
结论:
- 开发的DL模型有效地从12导心电图片中检测到HCM,克服了以前方法的局限性.
- 这种基于图像的方法在各种临床环境中促进了可扩展和可访问的HCM查.
- 这些发现支持整合人工智能驱动的心电图分析,以改进超性心肌病的检测.
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