1. Screening chronic kidney disease through deep learning utilizing ultra-wide-field fundus images.
- Author
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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, and Zhang, Hao
- Subjects
KIDNEY physiology ,COMPUTER simulation ,MEDICAL history taking ,PEARSON correlation (Statistics) ,RISK assessment ,RECEIVER operating characteristic curves ,PREDICTION models ,TERTIARY care ,DESCRIPTIVE statistics ,CHRONIC kidney failure ,LONGITUDINAL method ,DEEP learning ,RESEARCH methodology ,RESEARCH ,MEDICAL screening ,RETINA ,CONFIDENCE intervals ,DISEASE risk factors - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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