从角膜神经图像预测糖尿病脚的风险,使用深度学习算法
Chang Liu1, Lei Zhu2, Gavin Patrick O'Donnell3
1Cornea and Refractive Surgery Group, Singapore Eye Research Institute, Singapore; Regenerative Therapy Group, Singapore Eye Research Institute, Singapore.
Diabetes research and clinical practice
|November 8, 2025
概括
使用角膜神经图像和临床数据的深度学习算法有效预测糖尿病脚风险. 这种新的方法增强了糖尿病外围神经病变并发症的早期检测和分层.
科学领域:
- 眼科医生 眼科 眼科
- 糖尿病学 糖尿病学
- 人工智能的人工智能
背景情况:
- 糖尿病足是一种糖尿病的严重并发症.
- 早期检测和风险分层对于预防截肢至关重要.
- 目前评估糖尿病外围神经病变风险的方法存在局限性.
研究的目的:
- 开发一种深度学习算法 (DLA),用于预测糖尿病足风险类别.
- 评估单独使用角膜神经图像和与临床因素结合使用的疗效.
主要方法:
- 从471名参与者中使用23,550张角膜神经图像开发了DLA.
- 仅基于角膜图像的DLAs与结合临床数据 (HbA1c,血清肌) 的混合模型进行了比较.
- 使用接收器操作特征曲线 (AUC) 下面的面积来评估模型性能.
主要成果:
- 单独使用角膜图像的DLA实现了0.69的AUC (糖尿病足预测) 和0.76 (高风险识别).
- 综合HbA1c或血清肌氨酸的混合DLA分别达到0.94和0.93的AUC.
- 定量角膜神经参数并没有比单独的角膜图像提高性能.
结论:
- 将角膜神经图像与HbA1c或血清肌素相结合的DLA显示出糖尿病足风险预测的强大表现.
- 这种方法为糖尿病外围神经病变提供了一个有前途的新查策略.
- 这项研究强调了人工智能在治疗糖尿病并发症方面的潜力.
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