人工智能启用心电图学预测未来的心脏起器植入和不良心血管事件
Yuan Hung1, Chin Lin2,3,4, Chin-Sheng Lin1
1Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center Taipei, Taipei, Taiwan, R.O.C.
Journal of medical systems
|July 19, 2024
概括
一个新的深度学习模型 (DLM) 使用心电图数据预测未来的心脏起器植入 (PMI). 这种人工智能工具可以识别PMI,死亡率和心血管事件的高风险患者,从而能够更早地进行干预.
科学领域:
- 心脏病学 心脏病学
- 人工智能在医学中的应用
- 医学诊断 医学诊断 医学诊断
背景情况:
- 预期寿命的增加导致了更永久的起器植入物.
- 预测心脏起器植入 (PMI) 是具有挑战性的,因为在病性鼻综合征等疾病中存在非特异性症状.
- 电心电图 (ECG) 数据有可能预测未来的心血管事件和PMI的需要.
研究的目的:
- 开发一个深度学习模型 (DLM) 来从ECG数据中预测未来的PMI.
- 评估DLM预测心血管死亡率和发生心血管疾病的能力.
- 识别可预测PMI和心血管不良结果的关键ECG特征.
主要方法:
- 在来自学术医疗中心患者的158,471个心电图的大数据集上训练了一个DLM.
- 在来自医疗中心 (25,640名患者) 和社区医院 (26,538名患者) 的独立数据集上验证了DLM.
- 分析了90天内PMI的预测准确性,并使用危险比率评估了死亡率和心血管事件的风险.
主要成果:
- 在30日,60日和90天内,DLM在预测PMI方面取得了很高的准确性 (AUC为0.870-0.883),具有出色的灵敏度和特异性.
- 确定了重要的心电图预测因子,包括PR间隔,纠正的QT间隔,心率和QRS持续时间.
- 由AI-DLM识别的患者显示PMI,全因死亡率,心血管疾病死亡率和新心血管事件的风险显著增加.
结论:
- 使用心电图数据的深度学习模型可以准确地预测未来的起器植入.
- AI-DLM有效地识别了患有心血管不良结果和死亡风险较高的患者.
- 这种人工智能工具可以帮助临床医生及时干预患有PMI和相关并发症风险的患者.
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