元数据信息和 fundus 图像融合神经网络用于糖尿病中高尿素血症分类
Jin Wei1, Yupeng Xu1, Hanying Wang1
1Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Eye Diseases, Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, No. 100 Haining Road, Shanghai 20080, PR China.
Computer methods and programs in biomedicine
|August 30, 2024
概括
这项研究开发了一种深度学习算法,以使用视网膜图像和元数据在糖尿病患者中非侵入性检测高尿血症. 混合模型显示出高精度,为例行监测和预防糖尿病并发症提供了有前途的工具.
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:
- 超尿血症与糖尿病并发症,如大血管和小血管功能障碍有关.
- 目前血液尿酸测量是侵入性的,不适合在糖尿病患者的常规监测.
研究的目的:
- 开发一种非侵入性深度学习算法,用于使用视网膜照片和元数据检测糖尿病患者的高尿血.
- 评估算法在不同族裔群体和子群体中的表现.
主要方法:
- 开发了三个深度学习模型:仅用于元数据,仅用于图像 (基金照片) 和混合模型.
- 在高尿血症分类之前,包括估计的淋巴膜过率 (eGFR) 的回归.
- 在上海总医院糖尿病管理中心 (ShDMC) 的数据集上以及在英国生物银行数据集上进行训练和验证的模型.
主要成果:
- 混合模型在eGFR回归 (ShDMC的R2高达0.727,英国生物库的0.697) 和高尿血症分类 (ShDMC的AUC高达0.92,英国生物库的0.89) 两方面都取得了卓越的表现.
- 深度学习网络主要专注于光盘区域,以识别高尿血症.
结论:
- 使用 fundus 照片的深度学习算法显示,糖尿病患者的非侵入性超尿血症查具有潜力.
- 将患者元数据与视网膜图像相结合,可显著提高查准确度.
- 这种方法为糖尿病管理中的高尿素血常规监测提供了一个非侵入性的辅助方法.
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