通过比较人工智能支持的心电图模型来识别左心房扩大和长期心血管风险
Chung-Chuan Chou1, Zhi-Yong Liu2, Po-Cheng Chang1
1Division of Cardiology, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan, Taiwan; School of Medicine, Chang Gung University, Taoyuan, Taiwan.
The Canadian journal of cardiology
|January 1, 2024
概括
人工智能 (AI) 可以使用心电图 (ECG) 上的P波分析来识别左心房扩大 (LAE). 启用人工智能的心电图模型可以更准确地预测心血管疾病,而不是对严重的LAE进行心电图.
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:
- 人工智能支持的心电图模型对左心房扩大 (LAE) 的诊断能力尚不清楚.
- 人工智能支持的单导电心电图对于LAE监测的实用性需要进一步研究.
研究的目的:
- 开发支持人工智能的P波和单导电心电图模型,用于LAE检测.
- 将AI评估的严重LAE的预后性能与心声回声学进行比较.
主要方法:
- 对382,594名成年患者进行了回顾性分析,对应了心电图和心声回声.
- 使用UNet++进行P波细分,使用ResNet-18进行深度学习的ECG模型.
- 外部验证包括来自另外一家医院的11,753名患者.
主要成果:
- 支持人工智能的12导电图模型显示出优越的LAE分类性能.
- 单线AI启用心电图模型在50毫米的切线上显示了LAE检测的出色性能.
- 人工智能识别的严重LAE是心血管疾病的更强的预测因素,而不是心声学.
结论:
- P波分析对于人工智能驱动的使用心电图检测LAE至关重要.
- 与心声学相比,支持人工智能的心电图模型可以更好地预测与严重的LAE相关的心血管疾病.
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