Deep learning in estimating prevalence and systemic risk factors for diabetic retinopathy: a multi-ethnic study.

ABSTRACT: In any community, the key to understanding the burden of a specific condition is to conduct an epidemiological study. The deep learning system (DLS) recently showed promising diagnostic performance for diabetic retinopathy (DR). This study aims to use DLS as the grading tool, instead of human assessors, to determine the prevalence and the systemic cardiovascular risk factors for DR on fundus photographs, in patients with diabetes. This is a multi-ethnic (5 races), multi-site (8 datasets from Singapore, USA, Hong Kong, China and Australia), cross-sectional study involving 18,912 patients (n = 93,293 images). We compared these results and the time taken for DR assessment by DLS versus 17 human assessors - 10 retinal specialists/ophthalmologists and 7 professional graders). The estimat

SUBMITTER: Ting DSW 

PROVIDER: S-EPMC6550209 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature