ArticleJournal of the American Medical Informatics Association : JAMIA2023
Deep learning algorithms to detect diabetic kidney disease from retinal photographs in multiethnic populations with diabetes.
Article in Journal of the American Medical Informatics Association : JAMIA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 33 citations in OpenAlex.
- Risk factors for diabetic kidney disease in type 2 diabetes mellitus in Asia: a meta-analysis.Frontiers in endocrinology · 2026Pooled it
- Machine learning-based risk predictive models for diabetic kidney disease in type 2 diabetes mellitus patients: a systematic review and meta-analysis.Frontiers in endocrinology · 2025Pooled 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
- Review
- Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.International urology and nephrology · 2026Review
- DiFFA: A Diffusion-Based Model for Synthetic Fundus Fluorescein Angiography to Enhance Detection of Diabetic Microvascular Complication.Translational vision science & technology · 2026Observational
- Algor-Ethics in Diabetes Care: Mapping the Route.Diabetes/metabolism research and reviews · 2026Review
- Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics.Briefings in bioinformatics · 2026Article
- Development and Validation of a Novel Deep Learning-Based Model for Detection of Diabetic Kidney Disease from Retinal Imaging Using a Weighted Loss Method.Clinical ophthalmology (Auckland, N.Z.) · 2026Article
- Diagnostic value of urinary albumin creatinine ratio in predicting vision-threatening diabetic retinopathy in patients with diabetes: a systematic review and Meta-analysis.International journal of ophthalmology · 2026Article
- Diabetic kidney disease: integrating multi-omics insights, artificial intelligence, and novel therapeutics for precision medicine.Frontiers in genetics · 2026Review
- Prediction of advanced chronic kidney disease through retinal fundus images by deep learning.Scientific reports · 2025Article
- Use of artificial intelligence with retinal imaging in screening for diabetes-associated complications: systematic review.EClinicalMedicine · 2025Review
- Artificial intelligence approaches to enable early detection of CKD.Nature reviews. Nephrology · 2025Article
- Diagnosis of Chronic Kidney Disease Using Retinal Imaging and Urine Dipstick Data: Multimodal Deep Learning Approach.JMIR medical informatics · 2025Article
- Generative artificial intelligence: In the search for new landscapes in basic and clinical nephrology.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2025Article
- Harnessing gut-derived bioactives and AI diagnostics for the next generation of type 2 diabetes solutions.Frontiers in endocrinology · 2025Review
- Artificial intelligence-enhanced retinal imaging as a biomarker for systemic diseases.Theranostics · 2025Review
- Article
- Validation of neuron activation patterns for artificial intelligence models in oculomics.Scientific reports · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
19 authors at 7 institutions in 4 countries.
Funding
Abstract
objectiveTo develop a deep learning algorithm (DLA) to detect diabetic kideny disease (DKD) from retinal photographs of patients with diabetes, and evaluate performance in multiethnic populations. MATERIALS AND
methodsWe trained 3 models: (1) image-only; (2) risk factor (RF)-only multivariable logistic regression (LR) model adjusted for age, sex, ethnicity, diabetes duration, HbA1c, systolic blood pressure; (3) hybrid multivariable LR model combining RF data and standardized z-scores from image-only model. Data from Singapore Integrated Diabetic Retinopathy Program (SiDRP) were used to develop (6066 participants with diabetes, primary-care-based) and internally validate (5-fold cross-validation) the models. External testing on 2 independent datasets: (1) Singapore Epidemiology of Eye Diseases (SEED) study (1885 participants with diabetes, population-based); (2) Singapore Macroangiopathy and Microvascular Reactivity in Type 2 Diabetes (SMART2D) (439 participants with diabetes, cross-sectional) in Singapore. Supplementary external testing on 2 Caucasian cohorts: (3) Australian Eye and Heart Study (AHES) (460 participants with diabetes, cross-sectional) and (4) Northern Ireland Cohort for the Longitudinal Study of Ageing (NICOLA) (265 participants with diabetes, cross-sectional).
resultsIn SiDRP validation, area under the curve (AUC) was 0.826(95% CI 0.818-0.833) for image-only, 0.847(0.840-0.854) for RF-only, and 0.866(0.859-0.872) for hybrid. Estimates with SEED were 0.764(0.743-0.785) for image-only, 0.802(0.783-0.822) for RF-only, and 0.828(0.810-0.846) for hybrid. In SMART2D, AUC was 0.726(0.686-0.765) for image-only, 0.701(0.660-0.741) in RF-only, 0.761(0.724-0.797) for hybrid. DISCUSSION AND
conclusionThere is potential for DLA using retinal images as a screening adjunct for DKD among individuals with diabetes. This can value-add to existing DLA systems which diagnose diabetic retinopathy from retinal images, facilitating primary screening for DKD.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.