Deep learning algorithms to detect diabetic kidney disease from retinal photographs in multiethnic populations with diabetes Journal of the American Medical Informatics Association : JAMIA
Abstract Methods Results Discussion Conflicts of interest 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.
SHOW FULL ABSTRACT CITATION BIBTEX RIS
Bjorn Kaijun Betzler, Evelyn Yi Lyn Chee, Feng He et al.. Deep learning algorithms to detect diabetic kidney disease from retinal photographs in multiethnic populations with diabetes. Journal of the American Medical Informatics Association : JAMIA. (2023). https://doi.org/10.1093/jamia/ocad179 Bjorn Kaijun Betzler, Evelyn Yi Lyn Chee, Feng He, Cynthia Ciwei Lim, Jinyi Ho, Haslina Hamzah, Ngiap Chuan Tan, Gerald Liew, Gareth J McKay, Ruth E Hogg, Ian S Young, Ching-Yu Cheng, Su Chi Lim, Aaron Y Lee, Tien Yin Wong, Mong Li Lee, Wynne Hsu, Gavin Siew Wei Tan, Charumathi Sabanayagam
Yong Loo Lin School of Medicine, National University of Singapore, 117597, Singapore School of Computing, National University of Singapore, 117417, Singapore Singapore Eye Research Institute, Singapore National Eye Centre, 168751, Singapore Department of Renal Medicine, Singapore General Hospital, 168753, Singapore Singapore Eye Research Institute, Singapore National Eye Centre, 168751, Singapore Singapore Eye Research Institute, Singapore National Eye Centre, 168751, Singapore SingHealth Polyclinics, Singapore Health Services, 168582, Singapore Westmead Institute for Medical Research, University of Sydney, NSW 2145, Australia Centre for Public Health, Queen’s University Belfast, Belfast BT12 6BA, United Kingdom Centre for Public Health, Queen’s University Belfast, Belfast BT12 6BA, United Kingdom Centre for Public Health, Queen’s University Belfast, Belfast BT12 6BA, United Kingdom Singapore Eye Research Institute, Singapore National Eye Centre, 168751, Singapore Ophthalmology and Visual Science Academic Clinical Program, Duke-NUS Medical School, 169857, Singapore Khoo Teck Puat Hospital, 768828, Singapore Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore Saw Swee Hock School of Public Health, National University of Singapore, 117549, Singapore Department of Ophthalmology, University of Washington, Seattle, WA 98104, United States Singapore Eye Research Institute, Singapore National Eye Centre, 168751, Singapore Ophthalmology and Visual Science Academic Clinical Program, Duke-NUS Medical School, 169857, Singapore School of Computing, National University of Singapore, 117417, Singapore School of Computing, National University of Singapore, 117417, Singapore Singapore Eye Research Institute, Singapore National Eye Centre, 168751, Singapore Ophthalmology and Visual Science Academic Clinical Program, Duke-NUS Medical School, 169857, Singapore Singapore Eye Research Institute, Singapore National Eye Centre, 168751, Singapore Ophthalmology and Visual Science Academic Clinical Program, Duke-NUS Medical School, 169857, Singapore 