AI-Assisted identification of sex-specific patterns in diabetic retinopathy using retinal fundus images

Abstract

Diabetic retinopathy (DR) is a microvascular complication of diabetes that can lead to blindness if left untreated. Regular monitoring is crucial for detecting early signs of referable DR, and the progression to moderate to severe non-proliferative DR, proliferative DR (PDR), and macular edema (ME), the most common cause of vision loss in DR. Currently, aside from considerations during pregnancy, sex is not factored into DR diagnosis, management or treatment. Here we examine whether DR manifests differently in male and female patients, using a dataset of retinal images and leveraging convolutional neural networks (CNN) integrated with explainable artificial intelligence (AI) techniques. To minimize confounding variables, we curated 2,967 fundus images from a larger dataset of DR patients acquired from EyePACS, matching male and female groups for age, ethnicity, severity of DR, and hemoglobin A1C levels. Next, we fine-tuned two pre-trained VGG16 models-one trained on the ImageNet dataset and another on a sex classification task using healthy fundus images-achieving AUC scores of 0.72 and 0.75, respectively, both significantly above chance level. To uncover how these models distinguish between male and female retinas, we used the Guided Grad-CAM technique to generate saliency maps, highlighting critical retinal regions for correct classification. Saliency maps showed CNNs focused on different retinal regions by sex: the macula in females, and the optic disc and peripheral vasculature along the arcades in males. This pattern differed noticeably from the saliency maps generated by CNNs trained on healthy eyes. These findings raise the hypothesis that DR may manifest differently by sex, with women potentially at higher risk for developing ME, as opposed to men who may be at greater risk for PDR.

Fig 1. Saliency map results of sample fundus images from two male and two female patients for the ImageNet pre-trained model.

In each panel, the original fundus image, the Guided Grad-CAM output (3-channel image), and its color-coded amplitude (single-channel image) are shown from left to right.

Fig 2. Saliency map results of sample fundus images from two male and two female patients for the sex-trained model.

In each panel, the original fundus image, the Guided Grad-CAM output (3-channel image), and its colour-coded amplitude (single-channel image) are shown from left to right.