Regular screening for the early detection of common chronic diseases might benefit from the use of deep-learning approaches, particularly in resource-poor or remote settings. Here we show that deep-learning models can be used to identify chronic kidney disease and type 2 diabetes solely from fundus images or in combination with clinical metadata (age, sex, height, weight, body-mass index and blood pressure) with areas under the receiver operating characteristic curve of 0.85-0.93. The models were trained and validated with a total of 115,344 retinal fundus photographs from 57,672 patients and can also be used to predict estimated glomerulal filtration rates and blood-glucose levels, with mean absolute errors of 11.1-13.4 ml min-1 per 1.73 m2 and 0.65-1.1 mmol l-1, and to stratify patients according to disease-progression risk. We evaluated the generalizability of the models for the identification of chronic kidney disease and type 2 diabetes with population-based external validation cohorts and via a prospective study with fundus images captured with smartphones, and assessed the feasibility of predicting disease progression in a longitudinal cohort.
Nat Biomed Eng
533 - 545
Area Under Curve, Blood Glucose, Body Height, Body Mass Index, Body Weight, Deep Learning, Diabetes Mellitus, Type 2, Disease Progression, Female, Fundus Oculi, Glomerular Filtration Rate, Humans, Image Interpretation, Computer-Assisted, Male, Metadata, Middle Aged, Neural Networks, Computer, Photography, Prospective Studies, ROC Curve, Renal Insufficiency, Chronic, Retina