A deep learning model for screening type 2 diabetes from retinal photographs.

ABSTRACT:

Background and aims

We aimed to develop and evaluate a non-invasive deep learning algorithm for screening type 2 diabetes in UK Biobank participants using retinal images.

Methods and results

The deep learning model for prediction of type 2 diabetes was trained on retinal images from 50,077 UK Biobank participants and tested on 12,185 participants. We evaluated its performance in terms of predicting traditional risk factors (TRFs) and genetic risk for diabetes. Next, we compared the performance of three models in predicting type 2 diabetes using 1) an image-only deep learning algorithm, 2) TRFs, 3) the combination of the algorithm and TRFs. Assessing net reclassification improvement (NRI) allowed quantification of the improvement afforded by adding the algorithm to the TRF mo

SUBMITTER: Yun JS 

PROVIDER: S-EPMC9018521 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature