人工智能预测算法的临床适用性,用于早期预测非持久性心房动
Yeji Kim1, Gihun Joo2, Bo-Kyung Jeon1
1Cardiovascular Center, Department of Internal Medicine, College of Medicine, Ewha Womans University Medical Center, Seoul, Republic of Korea.
Frontiers in cardiovascular medicine
|October 2, 2023
概括
一个人工智能 (AI) 模型可以使用正常鼻节律 (SR) ECG 预测非持续性心房动 (非PeAF). 这种人工智能工具有助于早期风险分层和检测非PeAF,即使监测时间很短.
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:
- 在正常鼻节律 (SR) 期间从标准心电图中诊断非持久性心房动 (非PeAF) 是具有挑战性的.
- 关于AI能够从SR ECG数据中预测非PeAF的能力的理解有限.
研究的目的:
- 开发和验证用于选非PeAF的预测AI模型.
- 评估使用SR ECG在4周窗口内用于非PeAF预测的有效性.
主要方法:
- 追溯队列研究,涉及18-99岁的SR ECG患者.
- 利用基于1D-CNN的残余神经网络模型.
- 在非PeAF检测之前,在1周,2周和4周的窗口期内分析了SR心电图.
主要成果:
- 人工智能模型在1周的窗口中实现了0.862的AUC和0.84的F1得分.
- 性能指标 (AUC,F1得分) 在2周 (0.864,0.85) 和4周 (0.842,0.83) 窗口中保持高.
- 证明了算法对非PeAF的预测能力.
结论:
- 人工智能算法可以有效地从SR ECG中预测非PeAF,从而实现风险分层.
- 短的监测窗口时间 (例如,1-4周) 足以准确检测非PeAF.
- 人工智能模型为早期查和诊断非PeAF提供了一个有希望的工具.
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