Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study.
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Meng Ziyao, Guan Zhouyu, Yu Shujie, Wu Yilan, Zhao Yaoning, Shen Jie, Lim Cynthia Ciwei, Chen Tingli, Yang Dawei, Ran An Ran, He Feng, Hamzah Haslina, Singh Sarkaaj, Abd Raof Anis Syazwani, Lee-Boey Jian Wen Samuel, Lim Soo-Kun, Sun Xufang, Ge Shuwang, Xu Gang, Su Hua, Cheng Yang, Lu Feng, Liao Xiaofei, Jin Hai, Deng Chenxin, Ruan Lei, Zhang Cuntai, Wu Chan, Dai Rongping, Jin Yixiao, Wang Wenxiao, Li Tingyao, Liu Ruhan, Li Jiajia, Shu Jia, Lu Yuwei, Wang Xiangning, Wu Qiang, Qin Yiming, Tang Jin, Sheng Xiaohua, Jiao Qiong, Yang Xiaokang, Guo Minyi, McKay Gareth J, Hogg Ruth E, Liew Gerald, Chee Evelyn Yi Lyn, Hsu Wynne, Lee Mong Li, Szeto Simon, Luk Andrea O Y, Chan Juliana C N, Cheung Carol Y, Tan Gavin Siew Wei, Tham Yih-Chung, Cheng Ching-Yu, Sabanayagam Charumathi, Lim Lee-Ling, Jia Weiping, Li Huating, Sheng Bin, Wong Tien Yin
Аннотация
METHODS: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care System for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD; and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up.
FINDINGS: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838-0·846) on the internal validation dataset and AUCs of 0·791-0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825-0·966) on the internal validation dataset and AUCs of 0·733-0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD with higher sensitivity (89·8% vs 66·3%, p<0·0001). In the longitudinal study, participants with isolated diabetic nephropathy and participants with NDKD identified by DeepDKD had a significant difference in renal function outcomes (proportion of estimated glomerular filtration rate decline: 27·45% vs 52·56%, p=0·0010).
INTERPRETATION: Among diverse multi-ethnic populations with diabetes, a retinal image-based AI-deep learning system showed its potential for detecting DKD and differentiating isolated diabetic nephropathy from NDKD in clinical practice.
FUNDING: National Key R & D Program of China, National Natural Science Foundation of China, Beijing Natural Science Foundation, Shanghai Municipal Key Clinical Specialty, Shanghai Research Centre for Endocrine and Metabolic Diseases, Innovative research team of high-level local universities in Shanghai, Noncommunicable Chronic Diseases-National Science and Technology Major Project, Clinical Special Program of Shanghai Municipal Health Commission, and the three-year action plan to strengthen the construction of public health system in Shanghai.