Extended Data Fig. 1: Time-Dependent Analysis for Predicting Incident Stroke Event by Validation Cohorts.
From: A deep learning system for detecting silent brain infarction and predicting stroke risk
Shaded areas represent bootstrapped (n = 1,000) 95% confidence intervals. SDPP: Shanghai Diabetes Prevention Program; ECHM, The Eastern China Health Management; NDSP, Nicheng Diabetes Screening Project; WTHM, Wuhan Tongji Health Management; PUDM: Peking Union Diabetes Management; CUHK-STDR, The Chinese University of Hong Kong-Sight-Threatening Diabetic Retinopathy; SEED, the Singapore Epidemiology of Eye Diseases study; MeLODY, the Multiethnic Lifestyle, Obesity, and Diabetes Registry in Malaysia Diabetes Registry in Malaysia cohort; UKB, UK biobank; I-OPTA, Identification of patient-reported barriers to treatment with anti-VEGF for neovascular AMD; AREDS, Age-Related Eye Disease Study; AUC: area under the curve. CI: confidence interval.
