人工智能增强心电图方法的比较,用于使用心电图像预测死亡时间
Arunashis Sau1,2, Boroumand Zeidaabadi1, Konstantinos Patlatzoglou1
1National Heart and Lung Institute, Imperial College London, London, UK.
European heart journal. Digital health
|March 20, 2025
概括
人工智能心电图 (AI-ECG) 模型现在可以使用心电图像来预测死亡率,而不仅仅是数字数据. 这一突破使AI-ECG能够在缺乏数字ECG基础设施的全球环境中使用.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 大多数人工智能增强心电图 (AI-ECG) 模型需要数字存储的心电图来预测死亡率等不良事件.
- 大多数全球临床设施将心电图存储为图像,限制了当前AI-ECG模型的应用.
研究的目的:
- 开发和验证能够从心电图像中预测死亡率的AI-ECG模型.
- 评估在数字化心电图和心电图像上训练的模型的性能,与原始数字心电图相比.
主要方法:
- 训练1D卷积神经网络 (CNN) 模型在本地数字和PDF数字化心电图信号上,以预测死亡时间.
- 在心电图 (310 × 868 px) 上训练了一个2D CNN模型.
- 使用大型二级护理,巴西初级护理和查加斯病队伍的验证模型.
主要成果:
- 在心电图像上训练的2D CNN模型和在数字化心电图上训练的1D CNN模型在内部验证中显示了与在本地数字心电图上训练的模型相似的性能 (C指数~0.77-0.78).
- 外部验证证实了基于图像和数字化模型的可比性能.
- 在本地数字化1D心电图上训练的模型在应用到数字化1D心电图时表现类似.
结论:
- 使用2D CNN或数字化1D CNN模型的ECG图像进行AI-ECG死亡率预测是可行的,实现性能与本地数字化1D CNN模型相比.
- 在原生数字ECG上训练的模型可以有效地应用于数字化ECG,而不会显著降低性能.
- 这项研究将AI-ECG死亡率预测的适用性扩展到缺乏强大的数字ECG基础设施的各种全球环境.
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