Whole-body Representation Learning For Competing Preclinical Disease Risk Assessment

• URL: http://arxiv.org/abs/2508.02307v1
• Date: Mon, 04 Aug 2025 11:20:31 GMT
• Title: Whole-body Representation Learning For Competing Preclinical Disease Risk Assessment
• Authors: Dmitrii Seletkov, Sophie Starck, Ayhan Can Erdur, Yundi Zhang, Daniel Rueckert, Rickmer Braren,
• Abstract summary: We propose a whole-body self-supervised representation learning method for the preclinical disease risk assessment.<n>This approach outperforms whole-body radiomics in multiple diseases, including cardiovascular disease (CVD), type 2 diabetes (T2D), chronic obstructive pulmonary disease (COPD), and chronic kidney disease (CKD)<n>The results indicate the potential of whole-body representations as a standalone screening modality and as part of a multi-modal framework within clinical for early personalized risk stratification.
• Score: 10.200639509943443
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reliable preclinical disease risk assessment is essential to move public healthcare from reactive treatment to proactive identification and prevention. However, image-based risk prediction algorithms often consider one condition at a time and depend on hand-crafted features obtained through segmentation tools. We propose a whole-body self-supervised representation learning method for the preclinical disease risk assessment under a competing risk modeling. This approach outperforms whole-body radiomics in multiple diseases, including cardiovascular disease (CVD), type 2 diabetes (T2D), chronic obstructive pulmonary disease (COPD), and chronic kidney disease (CKD). Simulating a preclinical screening scenario and subsequently combining with cardiac MRI, it sharpens further the prediction for CVD subgroups: ischemic heart disease (IHD), hypertensive diseases (HD), and stroke. The results indicate the translational potential of whole-body representations as a standalone screening modality and as part of a multi-modal framework within clinical workflows for early personalized risk stratification. The code is available at https://github.com/yayapa/WBRLforCR/

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