使用人工智能启用心电图的可解释性心房动症诊断
Yeongbong Jin1, Bonggyun Ko2,3, Woojin Chang1
1Department of Industrial Engineering, Seoul National University, Seoul, Korea.
The Korean journal of internal medicine
|February 23, 2025
概括
人工智能算法可以从正常鼻节律 (NSR) 电心电图 (ECG) 预测动性心房动 (PAF) 的发生. 这种人工智能方法可以识别微妙的心电图变化,有助于早期检测和中风风险评估.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 心房动 (AF) 是致病率和死亡率的主要原因之一.
- 阴性心房动 (PAF) 通常是无声的,特别是在密码性中风患者中.
- 早期发现PAF对于预防中风至关重要.
研究的目的:
- 在正常鼻节律 (NSR) 患者中开发可靠的AI算法,用于早期AF检测.
- 使用12导电心电图 (ECG) 进行AF预测.
- 确定关键的心电图特征,表明未来的AF发作.
主要方法:
- 在552,372个心电图痕迹的大数据集上训练了深度神经网络.
- 使用接收器操作特征曲线 (AUROC) 下面的面积来评估模型性能.
- 雇佣可解释的AI来理解模型决策过程.
主要成果:
- 在早期PAF诊断中获得了0.905 ± 0.007的AUROC.
- 确定T波附近 (ST段,S峰) 对于PAF预测至关重要.
- 在NSR中发现了与PAF相关的非特异性ST-T异常和反转的T波.
结论:
- 深度学习模型可以从NSRECG预测AF发作.
- 人工智能模型识别了AF诊断的关键ECG特征.
- 这种方法可以作为PAF查和中风风险评估的预测工具.
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