Vision transformer based interpretable metabolic syndrome classification using retinal Images
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....
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01588-0 |
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| Summary: | 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. |
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| ISSN: | 2398-6352 |