基于心电图的深度学习用于慢性病检测和心血管风险预测
Ping-Huang Tsai1, Shang-Yang Lee2,3, Chia-Ling Helen Wei1,4
1Division of Nephrology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical University, Taipei, Taiwan, R.O.C.
BMC medical informatics and decision making
|December 3, 2025
概括
分析心电图 (ECG) 的深度学习模型 (DLM) 可以在实验室测试之前识别慢性病 (CKD) 风险. 这种人工智能工具有助于早期检测和风险分层,以获得更好的患者结果.
科学领域:
- 心脏病学 心脏病学
- 腎臟病學 (nephrology) 是一種醫學專業.
- 人工智能的人工智能
背景情况:
- 慢性病 (CKD) 是一个全球性的健康挑战,人们对此的认识很低.
- 深度学习模型 (DLM) 显示了用于疾病检测的ECG解释的潜力.
- 基于心电图的DLM可能为早期CKD识别提供新的途径.
研究的目的:
- 开发和验证用于使用心电图检测CKD的DLM.
- 评估DLM识别患有CKD及其并发症风险的个人的能力.
- 将DLM的预测性能与传统的eGFR分类进行比较.
主要方法:
- 使用了49632名门诊患者的心电图 (共66587名注册患者) 和eGFR数据 (2010年1月至2020年10月).
- 使用72,618个ECG开发了DLM,对16955名患者进行了内部验证,对10476名患者进行了外部验证.
- 主要结局:CKD检测 (eGFR<60mL/min/1.73m2);次要结局:所有原因死亡率和主要心血管事件.
主要成果:
- 对于CKD检测,DLM的AUC为0.885 (内部) 和0.861 (外部).
- DLM识别的CKD患者表现出更高的CKD进展和心血管疾病的风险.
- 在没有基线CKD的患者中,积极的DLM查显著增加了发生CKD的风险 (HRs 2.14和1.38).
- 在预测诸如中风,心力衰竭和心房动等不良结果方面,DLM分层表现优于eGFR.
结论:
- 基于心电图的DLM可以识别患有CKD和相关并发症风险的个体.
- 这种人工智能方法在临床环境中促进了早期检测和风险分层.
- 该模型在实验室标志物显现之前有望识别亚临床CKD.
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