开发一种以人工智能支持的心电图,以检测23种心律失常并预测心血管结果
Wen-Yu Lin1, Chin Lin2,3,4, Wen-Cheng Liu1
1Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center Taipei, Taiwan, R.O.C.
Journal of medical systems
|April 21, 2025
概括
一个新的深度学习模型 (DLM) 准确地检测出来自不同数据集的ECG的23个心律失常. 这种工具有助于医生识别高风险患者进行早期干预.
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:
- 心律失常是普遍存在的心脏病,影响有或没有潜在心脏病的个人.
- 深度学习模型 (DLM) 在解释心电图 (ECG) 进行心律失常检测方面表现有前途.
- 目前DLM的局限性包括限制性心律失常类型和数据集稳定性,阻碍了临床采用.
研究的目的:
- 开发和验证一个强大的深度学习模型 (DLM),用于检测广泛的心律失常.
- 评估DLM在多个多中心数据集中的表现.
- 评估DLM检测虚假阳性心律失常的长期临床影响.
主要方法:
- 算法开发使用了22130个ECG的大数据集,分为开发,调整,验证和竞争集.
- 在三个独立的开放数据集 (CODE-test,PTB-XL,CPSC2018) 上进行外部验证,共计32495个ECG.
- 对新发性心房动 (AF),心力衰竭 (HF) 和虚假阳性AF检测患者的死亡率的长期风险分析.
主要成果:
- 对于验证集中的大多数心律失常类,DLM实现了接收器运行特征曲线下的面积>0.97和>90%的灵敏度/特异性.
- DLM表现出心脏病学家水平的性能,在人机竞争中在平衡精度方面获得第一名.
- 外部验证证实了DLM在不同数据集中的强大和可比性能.
- 在调整后,假阳性AF检测与新发性AF (HR:1.69),HF (HR: 1.73) 和死亡率 (HR: 1.40) 的显著增加风险有关.
结论:
- 已经开发出一种准确的DLM,能够在多个数据集中检测23种心律失常.
- DLM显示出作为一种有价值的查工具的潜力,以帮助医生识别有风险的患者.
- 这项技术可以促进早期干预,并改善心律不整的风险分层.
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