Identification of Sex and Age from Macular Optical Coherence Tomography and Feature Analysis Using Deep Learning.

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Abstract

To develop deep learning models for identification of sex and age from macular optical coherence tomography (OCT), and to analyze the features for differentiation of sex and age.Algorithm development using database of macular OCT.One eye center in Taiwan.6147 sets of macular optical coherence tomography (OCT) images from the healthy eyes of 3134 persons.Deep learning based algorithms were used to develop models for identification of sex and age, and 10-fold cross-validation was applied. Gradient-weighted class activation mapping (Grad-CAM) was used for feature analysis.The accuracy for sex prediction using deep learning from macular OCT was 85.6±2.1%, compared to the accuracy of 61.9% by using macular thickness and 61.4±4.0% by using deep learning from infrared fundus photography (P<0.001 for both). The mean absolute error for age prediction using deep learning from macular OCT was 5.78±0.29 years. A thorough analysis of the prediction accuracy and the Grad-CAM showed that the cross-sectional foveal contour lead to a better sex distinction than the macular thickness or the fundus photography, and the age-related characteristics of macula were on the whole layers of retina, rather than the choroid.Sex and age could be identified from macular OCT using deep learning with good accuracy. The main sexual difference of macula lies in the foveal contour, and the whole layers of retina differ with aging. These novel findings provide useful information for further investigation in the pathogenesis of sex and age-related macular structural diseases.Copyright © 2021. Published by Elsevier Inc.