On the Deep Learning-based Age Prediction of Color Fundus Images and Correlation with Ophthalmic Diseases

Abstract

Color fundus imaging is an important modality used for ophthalmic disease screening and provides a non-invasive invivo method for assessing body condition. We aimed to assess deep learning models for age prediction from fundus images of normal patients and patients with ophthalmic diseases. In addition, we sought to investigate interpretable clues regarding the salient regions between normal and pathological changes as determined by deep learning models during age prediction. In this study, we used a convolutional neural network model for age prediction and evaluated it on an in-house database of fundus images of the Chinese population. The results of the experiment revealed some conclusions as follows: (1) deep learning-based classification models have better age prediction performance than deep learning-based regression models of fundus images; (2) deep learning-based models tend to use holistic information of the fundus for age prediction; (3) ophthalmic diseases that cause damage to the structure of the fundus and change its appearance will result in a decline in age prediction performance.