Sex-related difference in the retinal structure of young adults: a machine learning approach.

Purpose: To compare the accuracy of machine learning (ML) algorithms to classify the sex of the participant from retinal thickness datasets in different retinal layers.

Methods: This cross-sectional study involved 26 male and 38 female subjects. Data were acquired using HRA + OCT Spectralis, and the thickness and volume of 10 retinal layers were quantified.