Vision transformer based interpretable metabolic syndrome classification using retinal Images - Tae Kwan Lee; So Yeon Kim; Hyuk Jin Choi; Eun Kyung Choe; Kyung-Ah Sohn (npj Digital Medicine)
Abstract Metabolic syndrome is leading to an increased risk of diabetes and cardiovascular disease. Our study developed a model using retinal image data from fundus photographs taken during comprehensive health check-ups to classify metabolic syndrome. The model achieved an AUC of 0.7752 (95% CI: 0.7719–0.7786) using retinal images, and an AUC of 0.8725 (95% CI: 0.8669–0.8781) when combining retinal images with basic clinical features. Furthermore, we propose a method to improve the interpretability of the relationship between retinal image features and metabolic syndrome by visualizing metabolic syndrome-related areas in retinal images. The results highlight the potential of retinal images in classifying metabolic syndrome.