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<p>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.</p>

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Vision transformer based interpretable metabolic syndrome classification using retinal Images

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月11日

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  • Tae Kwan Lee,
  • So Yeon Kim,
  • Hyuk Jin Choi,
  • Eun Kyung Choe,
  • Kyung-Ah Sohn

摘要

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.