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Retina-enhanced multimodal deep learning for assessment of cardiovascular-kidney metabolic syndrome related outcomes.

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Title: Retina-enhanced multimodal deep learning for assessment of cardiovascular-kidney metabolic syndrome related outcomes.
Authors: Cheng, Di1,2 (AUTHOR), Liu, Dan1,2 (AUTHOR), Dong, Keqing3 (AUTHOR), Zhang, Yuren4 (AUTHOR), Cheng, Sichao5 (AUTHOR), Bao, Yuqian1,2 (AUTHOR), Li, Huating1,2 (AUTHOR), Liang, Zhen6,7 (AUTHOR) liangzhen@pkuszh.com, Jiang, Nan1,2 (AUTHOR) njiang2025@163.com, Jia, Weiping1,2 (AUTHOR) wpjia@sjtu.edu.cn
Source: Visual Computer. Jun2026, Vol. 42 Issue 8, p1-15. 15p.
Abstract: Cardiovascular-kidney-metabolic (CKM) syndrome requires integrated risk assessment, but traditional evaluation depends on numerous clinical variables and specialized examinations, limiting scalability. We developed a retina-enhanced multimodal deep learning framework integrating fundus photographs with eight routine clinical variables, incorporating two novel modules without manual segmentation: a multiscale vessel-background decoupling module that separates vascular and background features, and a clinical-guided residual rectification module that extracts retina-specific information complementary to clinical variables. Evaluated on 3619 participants for advanced CKM (ACKM), chronic kidney disease (CKD), and cardiovascular disease (CVD), our full model significantly outperformed all traditional clinical models, achieving AUC improvements from the best-performing clinical models (0.758 for ACKM, 0.692 for CKD, and 0.581 for CVD) to 0.812, 0.791, and 0.743, respectively. Ablation studies confirmed independent contributions of both modules, and feature perturbation tests verified reliance on genuine retinal spatial structures. This noninvasive, scalable framework offers a simplified alternative to conventional CKM evaluation, with strong potential for deployment in primary care and resource-limited settings. [ABSTRACT FROM AUTHOR]
Copyright of Visual Computer is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22Visual+Computer%22">Visual Computer</searchLink>. Jun2026, Vol. 42 Issue 8, p1-15. 15p.
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  Data: Cardiovascular-kidney-metabolic (CKM) syndrome requires integrated risk assessment, but traditional evaluation depends on numerous clinical variables and specialized examinations, limiting scalability. We developed a retina-enhanced multimodal deep learning framework integrating fundus photographs with eight routine clinical variables, incorporating two novel modules without manual segmentation: a multiscale vessel-background decoupling module that separates vascular and background features, and a clinical-guided residual rectification module that extracts retina-specific information complementary to clinical variables. Evaluated on 3619 participants for advanced CKM (ACKM), chronic kidney disease (CKD), and cardiovascular disease (CVD), our full model significantly outperformed all traditional clinical models, achieving AUC improvements from the best-performing clinical models (0.758 for ACKM, 0.692 for CKD, and 0.581 for CVD) to 0.812, 0.791, and 0.743, respectively. Ablation studies confirmed independent contributions of both modules, and feature perturbation tests verified reliance on genuine retinal spatial structures. This noninvasive, scalable framework offers a simplified alternative to conventional CKM evaluation, with strong potential for deployment in primary care and resource-limited settings. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Visual Computer is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Jun2026
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