基于图像的人工智能增强的心电图预测发生的心房动
Boroumand Zeidaabadi1, Konstantinos Patlatzoglou1, Joseph Barker1
1National Heart and Lung Institute, Imperial College London, United Kingdom.
通过人工智能增强的心电图 (AI-ECG) 模型,只使用心电图像可以预测心房动 (AF). 这种基于图像的AI-ECG方法提供了比现有的风险得分更高的性能,甚至可以使用智能手机图像.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 早期预测心房动 (AF) 对于预防不良结果至关重要.
- 人工智能增强心电图 (AI-ECG) 分析显示出有希望的结果,但通常需要数字心电图信号.
- 现有的方法在ECG作为图像存储的环境中是有限的.
研究的目的:
- 开发和验证基于图像的AI-ECG方法,用于预测事故AF.
- 评估模型在各种数据集和子组中的性能.
- 将AI-ECG模型的有效性与已建立的临床风险得分进行比较.
主要方法:
- 利用了来自189539名患者的1,163,401张心电图 (伯特以色列牧师医疗中心) 和来自65,610名参与者的70,655张心电图 (英国生物银行).
- 在经过310x868像素处理的ECG图像上训练了一种AI-ECG模型.
- 在子组和智能手机拍摄的心电图上验证了性能.
主要成果:
- 人工智能-心电图模型实现了0.754 (BIDMC) 和0.723 (英国生物库) 的C统计,用于预测事件AF.
- 与CHARGE-AF风险评分 (0.696比0.667) 相比,表现优越,并提供了附加值.
- 在各种各样的子组和智能手机捕获的心电图 (C-统计数据0.736) 上保持了表现.
- Saliency 映射表明关注 P 波形态和 PR 间隔.
结论:
- 基于图像的AI-ECG方法可以在没有数字ECG基础设施的情况下进行AF预测.
- 这种方法为现有的临床风险得分提供了附加值.
- 这种方法在各种临床环境中适用,包括那些使用智能手机捕获的心电图的人.
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