Application of a deep-learning marker for morbidity and mortality prediction derived from retinal photographs: a cohort development and validation study.

ABSTRACT:

Background

Biological ageing markers are useful to risk stratify morbidity and mortality more precisely than chronological age. In this study, we aimed to develop a novel deep-learning-based biological ageing marker (referred to as RetiPhenoAge hereafter) using retinal images and PhenoAge, a composite biomarker of phenotypic age.

Methods

We used retinal photographs from the UK Biobank dataset to train a deep-learning algorithm to predict the composite score of PhenoAge. We used a deep convolutional neural network architecture with multiple layers to develop our deep-learning-based biological ageing marker, as RetiPhenoAge, with the aim of identifying patterns and features in the retina associated with variations of blood biomarkers related to renal, immune, liver functions,

SUBMITTER: Nusinovici S 

PROVIDER: S-EPMC11788570 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature