1. Screening chronic kidney disease through deep learning utilizing ultra-wide-field fundus images
- Author
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Xinyu Zhao, Xingwang Gu, Lihui Meng, Yongwei Chen, Qing Zhao, Shiyu Cheng, Wenfei Zhang, Tiantian Cheng, Chuting Wang, Zhengming Shi, Shengyin Jiao, Changlong Jiang, Guofang Jiao, Da Teng, Xiaolei Sun, Bilei Zhang, Yakun Li, Huiqin Lu, Changzheng Chen, Hao Zhang, Ling Yuan, Chang Su, Han Zhang, Song Xia, Anyi Liang, Mengda Li, Dan Zhu, Meirong Xue, Dawei Sun, Qiuming Li, Ziwu Zhang, Donglei Zhang, Hongbin Lv, Rishet Ahmat, Zilong Wang, Charumathi Sabanayagam, Xiaowei Ding, Tien Yin Wong, and Youxin Chen
- Subjects
Computer applications to medicine. Medical informatics ,R858-859.7 - Abstract
Abstract 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.
- Published
- 2024
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