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- Title
Deep learning algorithms to detect diabetic kidney disease from retinal photographs in multiethnic populations with diabetes.
- Authors
Betzler, Bjorn Kaijun; Chee, Evelyn Yi Lyn; He, Feng; Lim, Cynthia Ciwei; Ho, Jinyi; Hamzah, Haslina; Tan, Ngiap Chuan; Liew, Gerald; McKay, Gareth J; Hogg, Ruth E; Young, Ian S; Cheng, Ching-Yu; Lim, Su Chi; Lee, Aaron Y; Wong, Tien Yin; Lee, Mong Li; Hsu, Wynne; Tan, Gavin Siew Wei; Sabanayagam, Charumathi
- Abstract
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.
- Subjects
SINGAPORE; MACHINE learning; DIABETIC nephropathies; DEEP learning; TYPE 2 diabetes; DIABETES; POPULATION of China
- Publication
Journal of the American Medical Informatics Association, 2023, Vol 30, Issue 12, p1904
- ISSN
1067-5027
- Publication type
Article
- DOI
10.1093/jamia/ocad179