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

Machine Learning (1501) Artificial Intelligence (1421) Imaging Informatics (200)

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 (25) 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
  2. Ranchod T. Systemic retinal biomarkers. Current Opinion in Ophthalmology 2021;32(5):439 View
  3. Betzler B, Rim T, Sabanayagam C, Cheng C. Artificial Intelligence in Predicting Systemic Parameters and Diseases From Ophthalmic Imaging. Frontiers in Digital Health 2022;4 View
  4. Iao W, Zhang W, Wang X, Wu Y, Lin D, Lin H. Deep Learning Algorithms for Screening and Diagnosis of Systemic Diseases Based on Ophthalmic Manifestations: A Systematic Review. Diagnostics 2023;13(5):900 View
  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
  6. Staffini A, Svensson T, Chung U, Svensson A. Heart Rate Modeling and Prediction Using Autoregressive Models and Deep Learning. Sensors 2021;22(1):34 View
  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

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