用人工智能增强的心电图用于从鼻腔节律预测心房动:跨机构和设备数据集成的影响
Shinya Suzuki1, Mari Amino2, Nobumoto Moriai3
1Department of Cardiovascular Medicine, The Cardiovascular Institute, Tokyo, Japan.
概括
在同质数据集上微调人工智能 (AI) 模型提高了从心电图 (ECG) 检测心房动 (AF) 的性能. 异质数据降低了准确性,但心电图系统格式的影响最小.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 人工智能 (AI) 越来越多地被用于从鼻腔节律心电图 (SR-ECGs) 检测阳动脉 (AF).
- 在不同机构和ECG系统中开发AI模型对于广泛的临床应用至关重要.
- 由于数据异质性,以前的模型往往缺乏通用性.
研究的目的:
- 研究数据同质性和心电图系统差异对AI增强的心电图模型用于AF检测的性能的影响.
- 将从头开始训练的模型与使用多个机构和心电图系统的数据微调的模型进行比较.
- 评估AI模型在不同ECG数据格式中的通用性.
主要方法:
- 开发了AI模型,使用来自两个拥有不同心电图谱系统的机构 (Nihon Kohden和GE MUSE) 的大规模SR-ECG数据集.
- 根据临床随访数据定义了AF标记和SR标记的心电图.
- 创建了不同的培训和验证数据集,包括从头开始训练的模型和微调模型,并使用各种ECG分辨率和压缩格式评估性能.
主要成果:
- 在同质的心脏病科数据上训练的微调模型 (F2) 实现了高性能 (AUC从0.829到0.885不等).
- 在异质数据上训练的微调模型 (F1) 显示出显著较低的性能 (AUC从0.660到0.837).
- 模型性能在不同的心电图数据格式和压缩类型中保持一致.
结论:
- 在同质数据集上微调AI模型可以提高AF检测的性能和通用性.
- 机构和部门之间的数据异质性会对模型准确性产生负面影响.
- 电脑心电图系统格式的变化对训练有素的人工智能模型的性能影响微不足道.
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