ArticleJMIR medical informatics2020
Deep Learning-Based Detection of Early Renal Function Impairment Using Retinal Fundus Images: Model Development and Validation.
Article in JMIR medical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 53 citations in OpenAlex.
- 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 · 2023Pooled it
- AI-driven multimodal retinal imaging for early detection and risk stratification of vascular and neurodegenerative diseases.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Transforming Fundus Photography for Deep Learning-Based Anemia Screening.Journal of clinical medicine · 2026Article
- Artificial intelligence framework for multi-pathology risk assessment from retinal fundus images: deep learning approach to 15-disease screening.Frontiers in medicine · 2026Article
- Development and validation of a deep learning model for early detection and screening of diabetic retinopathy.BMC medical informatics and decision making · 2025Article
- Grading of Foveal Hypoplasia Using Deep Learning on Retinal Fundus Images.Translational vision science & technology · 2025Article
- Use of artificial intelligence with retinal imaging in screening for diabetes-associated complications: systematic review.EClinicalMedicine · 2025Review
- Predicting renal function using fundus photography: role of confounders.The Korean journal of internal medicine · 2025Article
- Diagnosis of Chronic Kidney Disease Using Retinal Imaging and Urine Dipstick Data: Multimodal Deep Learning Approach.JMIR medical informatics · 2025Article
- Article
- Artificial intelligence-enhanced retinal imaging as a biomarker for systemic diseases.Theranostics · 2025Review
- Evaluating the potential of retinal photography in chronic kidney disease detection: a review.PeerJ · 2024Review
- Short-term duration of diabetic retinopathy as a predictor for development of diabetic kidney disease.Journal of translational internal medicine · 2023Article
- Deep learning algorithms to detect diabetic kidney disease from retinal photographs in multiethnic populations with diabetes.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Ocular images-based artificial intelligence on systemic diseases.Biomedical engineering online · 2023Review
- Deep Learning Algorithms for Screening and Diagnosis of Systemic Diseases Based on Ophthalmic Manifestations: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Deep learning-based fundus image analysis for cardiovascular disease: a review.Therapeutic advances in chronic disease · 2023Review
- Lesion-aware attention network for diabetic nephropathy diagnosis with optical coherence tomography images.Frontiers in medicine · 2023Article
- Article
- 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 · 2022Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRetinal imaging has been applied for detecting eye diseases and cardiovascular risks using deep learning-based methods. Furthermore, retinal microvascular and structural changes were found in renal function impairments. However, a deep learning-based method using retinal images for detecting early renal function impairment has not yet been well studied.
objectiveThis study aimed to develop and evaluate a deep learning model for detecting early renal function impairment using retinal fundus images.
methodsThis retrospective study enrolled patients who underwent renal function tests with color fundus images captured at any time between January 1, 2001, and August 31, 2019. A deep learning model was constructed to detect impaired renal function from the images. Early renal function impairment was defined as estimated glomerular filtration rate <90 mL/min/1.73 m
resultsIn total, 25,706 retinal fundus images were obtained from 6212 patients for the study period. The images were divided at an 8:1:1 ratio. The training, validation, and testing data sets respectively contained 20,787, 2189, and 2730 images from 4970, 621, and 621 patients. There were 10,686 and 15,020 images determined to indicate normal and impaired renal function, respectively. The AUC of the model was 0.81 in the overall population. In subgroups stratified by serum hemoglobin A
conclusionsThe deep learning model in this study enables the detection of early renal function impairment using retinal fundus images. The model was more accurate for patients with elevated serum HbA
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