利用超广角眼底图像通过深度学习筛查慢性肾病。
Screening chronic kidney disease through deep learning utilizing ultra-wide-field fundus images.
作者信息
Zhao Xinyu, Gu Xingwang, Meng Lihui, Chen Yongwei, Zhao Qing, Cheng Shiyu, Zhang Wenfei, Cheng Tiantian, Wang Chuting, Shi Zhengming, Jiao Shengyin, Jiang Changlong, Jiao Guofang, Teng Da, Sun Xiaolei, Zhang Bilei, Li Yakun, Lu Huiqin, Chen Changzheng, Zhang Hao, Yuan Ling, Su Chang, Zhang Han, Xia Song, Liang Anyi, Li Mengda, Zhu Dan, Xue Meirong, Sun Dawei, Li Qiuming, Zhang Ziwu, Zhang Donglei, Lv Hongbin, Ahmat Rishet, Wang Zilong, Sabanayagam Charumathi, Ding Xiaowei, Wong Tien Yin, Chen Youxin
机构信息
Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China and Key Lab of Ocular Fundus Diseases, Chinese Academy of Medical Sciences, Beijing, China.
Department of Research, VoxelCloud, Shanghai, China.
出版信息
NPJ Digit Med. 2024 Oct 7;7(1):275. doi: 10.1038/s41746-024-01271-w.
To address challenges in screening for chronic kidney disease (CKD), we devised a deep learning-based CKD screening model named UWF-CKDS. It utilizes ultra-wide-field (UWF) fundus images to predict the presence of CKD. We validated the model with data from 23 tertiary hospitals across China. Retinal vessels and retinal microvascular parameters (RMPs) were extracted to enhance model interpretability, which revealed a significant correlation between renal function and RMPs. UWF-CKDS, utilizing UWF images, RMPs, and relevant medical history, can accurately determine CKD status. Importantly, UWF-CKDS exhibited superior performance compared to CTR-CKDS, a model developed using the central region (CTR) cropped from UWF images, underscoring the contribution of the peripheral retina in predicting renal function. The study presents UWF-CKDS as a highly implementable method for large-scale and accurate CKD screening at the population level.
为应对慢性肾脏病（CKD）筛查中的挑战，我们设计了一种基于深度学习的CKD筛查模型，名为UWF-CKDS。它利用超广角（UWF）眼底图像来预测CKD的存在。我们用来自中国23家三级医院的数据对该模型进行了验证。提取视网膜血管和视网膜微血管参数（RMPs）以增强模型的可解释性，这揭示了肾功能与RMPs之间的显著相关性。UWF-CKDS利用UWF图像、RMPs和相关病史，可以准确确定CKD状态。重要的是，与使用从UWF图像中裁剪出的中心区域（CTR）开发的模型CTR-CKDS相比，UWF-CKDS表现出更优的性能，突出了周边视网膜在预测肾功能方面的作用。该研究将UWF-CKDS作为一种在人群层面进行大规模、准确CKD筛查的高度可实施方法。
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