Age and sex affect deep learning prediction of cardiometabolic risk factors from retinal images.

ABSTRACT: Deep neural networks can extract clinical information, such as diabetic retinopathy status and individual characteristics (e.g. age and sex), from retinal images. Here, we report the first study to train deep learning models with retinal images from 3,000 Qatari citizens participating in the Qatar Biobank study. We investigated whether fundus images can predict cardiometabolic risk factors, such as age, sex, blood pressure, smoking status, glycaemic status, total lipid panel, sex steroid hormones and bioimpedance measurements. Additionally, the role of age and sex as mediating factors when predicting cardiometabolic risk factors from fundus images was studied. Predictions at person-level were made by combining information of an optic disc centred and a macula centred image of both eyes wit

SUBMITTER: Gerrits N 

PROVIDER: S-EPMC7287116 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature