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. A total of 10 features were extracted from each retinal layer. The accuracy of various algorithms, including k-nearest-neighbor, support vector classifier, logistic regression, linear discriminant analysis, random forest, decision tree, and Gaussian Naïve Bayes, was quantified. A two-way ANOVA was conducted to assess the ML accuracy, considering both the classifier type and the retinal layer as factors.

Results: A comparison of the accuracies achieved by various algorithms in classifying participant sex revealed superior results in datasets related to total retinal thickness and the retinal nerve fiber layer. In these instances, no significant differences in algorithm performance were observed ( > 0.05). Conversely, in other layers, a decrease in classification accuracy was noted as the layer moved outward in the retina. Here, the random forest (RF) algorithm demonstrated superior performance compared to the others ( < 0.05).

Conclusion: The current research highlights the distinctive potential of various retinal layers in sex classification. Different layers and ML algorithms yield distinct accuracies. The RF algorithm's consistent superiority suggests its effectiveness in identifying sex-related features from a range of retinal layers.