This paper is in the following e-collection/theme issue:

Machine Learning (1770) Artificial Intelligence (1781) Imaging Informatics (240)

Published on in Vol 8, No 11 (2020): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23472, first published .
Deep Learning–Based Detection of Early Renal Function Impairment Using Retinal Fundus Images: Model Development and Validation

Deep Learning–Based Detection of Early Renal Function Impairment Using Retinal Fundus Images: Model Development and Validation

Deep Learning–Based Detection of Early Renal Function Impairment Using Retinal Fundus Images: Model Development and Validation

Authors of this article:

Eugene Yu-Chuan Kang1, 2 Author Orcid Image ;   Yi-Ting Hsieh3 Author Orcid Image ;   Chien-Hung Li4 Author Orcid Image ;   Yi-Jin Huang4 Author Orcid Image ;   Chang-Fu Kuo2, 5 Author Orcid Image ;   Je-Ho Kang6 Author Orcid Image ;   Kuan-Jen Chen1, 2 Author Orcid Image ;   Chi-Chun Lai1, 2 Author Orcid Image ;   Wei-Chi Wu1, 2 Author Orcid Image ;   Yih-Shiou Hwang1, 2 Author Orcid Image
Article Authors Cited by (31) Tweetations (1) Metrics

Journals

  1. Kang E, Yeung L, Lee Y, Wu C, Peng S, Chen Y, Gao Q, Lin C, Kuo C, Lai C. A Multimodal Imaging–Based Deep Learning Model for Detecting Treatment-Requiring Retinal Vascular Diseases: Model Development and Validation Study. JMIR Medical Informatics 2021;9(5):e28868 View
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  5. Park S, Ko T, Park C, Kim Y, Choi I. Deep Learning Model Based on 3D Optical Coherence Tomography Images for the Automated Detection of Pathologic Myopia. Diagnostics 2022;12(3):742 View
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  7. Lim W, Ho H, Ho H, Chen Y, Lee C, Chen P, Lai F, Jang J, Ko M. Use of multimodal dataset in AI for detecting glaucoma based on fundus photographs assessed with OCT: focus group study on high prevalence of myopia. BMC Medical Imaging 2022;22(1) View
  8. Wen J, Liu D, Wu Q, Zhao L, Iao W, Lin H. Retinal image‐based artificial intelligence in detecting and predicting kidney diseases: Current advances and future perspectives. VIEW 2023;4(3) View
  9. Tan Y, Ma Y, Rao S, Sun X. Performance of deep learning for detection of chronic kidney disease from retinal fundus photographs: A systematic review and meta-analysis. European Journal of Ophthalmology 2024;34(2):502 View
  10. Khan R, Surya J, Roy M, Swathi Priya M, Mohan S, Raman S, Raman A, Vyas A, Raman R. Use of artificial intelligence algorithms to predict systemic diseases from retinal images. WIREs Data Mining and Knowledge Discovery 2023;13(5) View
  11. Liu Y, Zhang F, Gao X, Liu T, Dong J. Lesion-aware attention network for diabetic nephropathy diagnosis with optical coherence tomography images. Frontiers in Medicine 2023;10 View
  12. Betzler B, Chee E, He F, Lim C, Ho J, Hamzah H, Tan N, Liew G, McKay G, Hogg R, Young I, Cheng C, Lim S, Lee A, Wong T, Lee M, Hsu W, Tan G, Sabanayagam C. Deep learning algorithms to detect diabetic kidney disease from retinal photographs in multiethnic populations with diabetes. Journal of the American Medical Informatics Association 2023;30(12):1904 View
  13. Tan Y, Sun X. Ocular images-based artificial intelligence on systemic diseases. BioMedical Engineering OnLine 2023;22(1) View
  14. Hu W, Yii F, Chen R, Zhang X, Shang X, Kiburg K, Woods E, Vingrys A, Zhang L, Zhu Z, He M. A Systematic Review and Meta-Analysis of Applying Deep Learning in the Prediction of the Risk of Cardiovascular Diseases From Retinal Images. Translational Vision Science & Technology 2023;12(7):14 View
  15. Chikumba S, Hu Y, Luo J. Deep learning-based fundus image analysis for cardiovascular disease: a review. Therapeutic Advances in Chronic Disease 2023;14 View
  16. An S, Vaghefi E, Yang S, Xie L, Squirrell D, Sasso F. Examination of alternative eGFR definitions on the performance of deep learning models for detection of chronic kidney disease from fundus photographs. PLOS ONE 2023;18(11):e0295073 View
  17. Duan J, Liu D, Zhao Z, Liang L, Pan S, Tian F, Yu P, Li G, Liu Z. Short-term duration of diabetic retinopathy as a predictor for development of diabetic kidney disease. Journal of Translational Internal Medicine 2023;11(4):449 View
  18. Miao H, Zou Z, Xu J, Gao Y. Advancing systemic disease diagnosis through ophthalmic image‐based artificial intelligence. MedComm – Future Medicine 2024;3(1) View
  19. Lin W, Wang P, Qi Y, Zhao Y, Wei X. Progress and challenges of in vivo flow cytometry and its applications in circulating cells of eyes. Cytometry Part A 2024;105(6):437 View
  20. Omar M, Abad Ali M, Qabillie S, Haji A, Takriti M, Atif A, Rangraze I. Beyond Vision: Potential Role of AI-enabled Ocular Scans in the Prediction of Aging and Systemic Disorders. Siriraj Medical Journal 2024;76(2):106 View
  21. Amir Hamzah N, Wan Zaki W, Wan Abdul Halim W, Mustafar R, Saad A. Evaluating the potential of retinal photography in chronic kidney disease detection: a review. PeerJ 2024;12:e17786 View
  22. Bhak Y, Lee Y, Kim J, Lee K, Lee D, Jang E, Jang E, Lee C, Kang E, Park S, Han H, Nam S. Diagnosis of Chronic Kidney Disease Using Retinal Imaging and Urine Dipstick Data: Multimodal Deep Learning Approach. JMIR Medical Informatics 2025;13:e55825 View
  23. Yang Q, Bee Y, Lim C, Sabanayagam C, Yim-Lui Cheung C, Wong T, Ting D, Lim L, Li H, He M, Lee A, Shaw A, Keong Y, Wei Tan G. Use of artificial intelligence with retinal imaging in screening for diabetes-associated complications: systematic review. eClinicalMedicine 2025;81:103089 View
  24. Zhu Z, Wang Y, Qi Z, Hu W, Zhang X, Wagner S, Wang Y, Ran A, Ong J, Waisberg E, Masalkhi M, Suh A, Tham Y, Cheung C, Yang X, Yu H, Ge Z, Wang W, Sheng B, Liu Y, Lee A, Denniston A, Wijngaarden P, Keane P, Cheng C, He M, Wong T. Oculomics: Current concepts and evidence. Progress in Retinal and Eye Research 2025;106:101350 View
  25. Park H, Kim H, Nam K, Kim B, Kang T. Predicting renal function using fundus photography: role of confounders. The Korean Journal of Internal Medicine 2025;40(2):310 View
  26. Fang P, Wu Y, He Y, Li H, Guan Z, Wang X, Chen T, Shen J. Research progress on AI-assisted screening and prediction of systemic diseases based on retinal images. The Visual Computer 2025 View
  27. Tsai T, Chang Y, Kang E, Chen K, Wang N, Liu L, Hwang Y, Lai C, Chen S, Chen J, Lai C, Wu W. Grading of Foveal Hypoplasia Using Deep Learning on Retinal Fundus Images. Translational Vision Science & Technology 2025;14(5):18 View

Books/Policy Documents

  1. Betzler B, Rim T, Cheung C, Wong T, Cheng C. Digital Eye Care and Teleophthalmology. View
  2. Ran A, Hui H, Cheung C, Wong T. Artificial Intelligence in Clinical Practice. View
  3. Charonis A, Perpatari E, Charonis A. Precision Health in the Digital Age. View

Conference Proceedings

  1. Yu H, Xin J, Wu J, Wang L, Chen S, Zuo W, Cui W, Zheng N. 2022 China Automation Congress (CAC). Intelligent Diagnosis of Diabetic Kidney Disease Based on Heterogeneous Data View