电图特征改善了多式联络深度学习,在中东群体中预测T2DM事件的多式联络深度学习
Farida Mohsen1, Ali Safa1, Zubair Shah2
1College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
Scientific reports
|July 27, 2025
概括
一个新的深度学习模型,ECG-DiaNet,将心电图 (ECG) 数据与临床风险因素相结合,用于预测2型糖尿病 (T2DM) 发病. 这种多式联络方法显著提高了预测准确性,为早期T2DM风险评估提供了有前途的工具.
科学领域:
- 生物医学工程 生物医学工程
- 心脏病学 心脏病学
- 内分泌学 在内分泌学.
背景情况:
- 2型糖尿病 (T2DM) 构成了全球健康的重大负担,需要先进的早期检测方法.
- 目前的风险预测模型通常仅依赖临床风险因素 (CRF),可能会限制其预测能力.
研究的目的:
- 开发和验证多式联络深度学习模型ECG-DiaNet,以更好地预测T2DM发病.
- 评估整合心电图 (ECG) 功能与CRF用于T2DM风险预测的附加值.
主要方法:
- 开发了一个深度学习模型 (ECG-DiaNet),使用来自卡塔尔生物银行 (QBB) 的ECG信号和CRF.
- 模型的性能被评估在一个发展队列 (n=2043) 和一个纵向测试队列 (n=395) 中,随访时间中位数为5年.
- ECG-DiaNet与仅使用ECG或CRF的单模模型进行了比较.
主要成果:
- 与仅使用CRF的模型 (0.8217,p < 0.001) 相比,ECG-DiaNet表现出优异的预测性能,接收器运行特征曲线 (AUROC) 下面的面积更高,为0.845.
- 在净重新分类改进 (NRI = 0.0153,p < 0.001) 和综合歧视改进 (IDI = 0.0482,p = 0.0099) 中观察到显著改善.
- 与仅使用CRF的模型相比,ECG-DiaNet在高风险组中显示出更高的正预测值 (PPV).
结论:
- 通过ECG-DiaNet将ECG特征与CRF集成,可以显著提高T2DM发病预测.
- 电脑心电图的非侵入性和可访问性使得ECG-DiaNet成为临床风险分层的潜在有价值工具.
- 建议对更大,更多样化的数据集进行进一步验证,以确保可通用性.
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