Vision transformer based interpretable metabolic syndrome classification using retinal Images.
cross_sectional · Level IV
Where this comes from
- Record sourced from PubMed, PMID 40216912.
- Also identified by DOI 10.1038/s41746-025-01588-0 and PMC identifier 11992118.
- Licence recorded as CC BY-NC-ND.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
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.