npj Digital Medicine (Oct 2024)
Screening chronic kidney disease through deep learning utilizing ultra-wide-field fundus images
- 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,
- Youxin Chen
Affiliations
- Xinyu Zhao
- 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
- Xingwang Gu
- 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
- Lihui Meng
- 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
- Yongwei Chen
- Department of Research, VoxelCloud
- Qing Zhao
- 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
- Shiyu Cheng
- 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
- Wenfei Zhang
- 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
- Tiantian Cheng
- 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
- Chuting Wang
- 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
- Zhengming Shi
- 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
- Shengyin Jiao
- Department of Research, VoxelCloud
- Changlong Jiang
- Department of Research, VoxelCloud
- Guofang Jiao
- Tonghua Eye Hospital of Jilin Province
- Da Teng
- Department of Ophthalmology, Beijing Tiantan Hospital, Capital Medical University
- Xiaolei Sun
- Eye Hospital of Shandong First Medical University (Shandong Eye Hospital)
- Bilei Zhang
- Department of Ophthalmology, Hunan Provincial People’s Hospital, The First Affiliated Hospital of Hunan Normal University
- Yakun Li
- Department of Ophthalmology, The Second Affiliated Hospital of Hebei North University
- Huiqin Lu
- Department of Ophthalmology, Xi’an No. 1 Hospital
- Changzheng Chen
- Eye Center, Renmin Hospital of Wuhan University
- Hao Zhang
- Department of Ophthalmology, The Fourth People’s Hospital of Shenyang, China Medical University
- Ling Yuan
- Department of Ophthalmology, The First Affiliated Hospital of Kunming Medical University
- Chang Su
- Department of Ophthalmology, Affiliated Hospital of Chengde Medical University
- Han Zhang
- Department of Ophthalmology, The First Hospital of China Medical University
- Song Xia
- Department of Ophthalmology, Guizhou Provincial People’s Hospital
- Anyi Liang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University
- Mengda Li
- Eye Center, Beijing Tsinghua Changgung Hospital, Beijing, China and School of Clinical Medicine, Tsinghua University
- Dan Zhu
- Department of Ophthalmology, The Affiliated Hospital of Inner Mongolia Medical University
- Meirong Xue
- Department of Ophthalmology, Hainan Hospital of PLA General Hospital
- Dawei Sun
- Department of Ophthalmology, The Second Affiliated Hospital, Harbin Medical Medical
- Qiuming Li
- Department of Ophthalmology, The First Affiliated Hospital of Zhengzhou University
- Ziwu Zhang
- Department of Ophthalmology, Fujian Medical University Union Hospital
- Donglei Zhang
- Department of Ophthalmology, The First Affiliated Hospital of Shanxi Medical University
- Hongbin Lv
- Department of Ophthalmology, The Affiliated Hospital of Southwest Medical University
- Rishet Ahmat
- Department of Ophthalmology, Bayinguoleng People’s Hospital
- Zilong Wang
- Microsoft Research Asia (Shanghai)
- Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore and National Eye Centre
- Xiaowei Ding
- Department of Research, VoxelCloud
- Tien Yin Wong
- Eye Center, Beijing Tsinghua Changgung Hospital, Beijing, China and School of Clinical Medicine, Tsinghua University
- Youxin Chen
- 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
- DOI
- https://doi.org/10.1038/s41746-024-01271-w
- Journal volume & issue
-
Vol. 7,
no. 1
pp. 1 – 11
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.
