Deep learning algorithms to detect diabetic kidney disease from retinal photographs in multiethnic populations with diabetes
Open accessAbstract
Objective: To 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 methods: We 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). Results: In 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 conclusion: There 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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Cross-modality prediction for DN is feasible.
“indicating the feasibility of cross-modality prediction for DN”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [75]
A DL model incorporating retinal fundus images alongside age and sex achieved AUCs of 0.866 internally and up to 0.828 in external validation cohorts.
“a DL model incorporating retinal fundus images alongside age and sex achieved AUCs of 0.866 internally and up to 0.828 in external validation cohorts”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [75]
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- Authors
- Betzler, B. K., Chee, E. Y. L., He, F., Lim, C. C., Ho, J., Hamzah, H., Tan, N. C., Liew, G., McKay, G. J., Hogg, R. E., Young, I. S., Cheng, C.-Y., Lim, S. C., Lee, A. Y., Wong, T. Y., Lee, M. L., Hsu, W., Tan, G. S. W., & Sabanayagam, C.
- Affiliations
- National University of Singapore (SG)
- Singapore National Eye Center (SG)
- Singapore General Hospital (SG)
- SingHealth (SG)
- The University of Sydney (AU)
- Queen's University Belfast (GB)
- University of Washington (US)
- Corresponding author
- Sabanayagam, Charumathi
- ORCID
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2023-09-02
- Volume
- 30
- Issue
- 12
- Pages
- 1904-1914
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC10654858
- OpenAlex
- W4386387048
- Cited by
- 42
- References
- 34
- Citations by year
- 2026: 15 · 2025: 19 · 2024: 8
- FWCI
- 6.96
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of the American Medical Informatics Association (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Portal (Queen's University Belfast) (acceptedVersion, cc-by)
- DR-NTU (Nanyang Technological University) (submittedVersion)
- Funders
- National Medical Research Council (NMRC/STaR/003/2008, NMRC/OFLCG/001/2017, NMRC/MOH-HCSAINV21jun-0001)
- National Research Foundation (AISG-GC-2019-001-2A)
- Economic and Social Research Council
- UKCRC Centre of Excellence for Public Health Northern Ireland
- Centre for Ageing Research and Development in Ireland
- Office of the First Minister and Deputy First Minister
- Health and Social Care Research and Development Division of the Public Health Agency
- Wellcome Trust
- Wolfson Foundation, and Queen’s University Belfast
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Health Sciences › Medicine › Nephrology › Chronic Kidney Disease and Diabetes
- Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
- Keywords
- diabetic nephropathy, fundus images, deep learning algorithms, retinal imaging, diabetes mellitus, multi-ethnic population, risk factors, hybrid model, diabetes duration, HbA1c, systolic blood pressure, type 2 diabetes, diabetic retinopathy, external validation
- MeSH terms
- Deep Learning, Algorithms, Australia, Cross-Sectional Studies, Diabetes Mellitus, Type 2, Diabetic Nephropathies, Diabetic Retinopathy, Humans, Longitudinal Studies
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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