A generalizable large-scale foundation model for musculoskeletal radiographs

• URL: http://arxiv.org/abs/2602.03076v1
• Date: Tue, 03 Feb 2026 04:04:45 GMT
• Title: A generalizable large-scale foundation model for musculoskeletal radiographs
• Authors: Shinn Kim, Soobin Lee, Kyoungseob Shin, Han-Soo Kim, Yongsung Kim, Minsu Kim, Juhong Nam, Somang Ko, Daeheon Kwon, Wook Huh, Ilkyu Han, Sunghoon Kwon,
• Abstract summary: We present SKELEX, a large-scale foundation model for musculoskeletal radiographs trained using self-supervised learning.<n>The model was evaluated on 12 downstream diagnostic tasks and generally outperformed baselines in fracture detection, osteoarthritis grading, and bone tumor classification.<n>We developed an interpretable, region-guided model for predicting bone tumors, which maintained robust performance on independent external datasets.
• Score: 6.440881664328117
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Artificial intelligence (AI) has shown promise in detecting and characterizing musculoskeletal diseases from radiographs. However, most existing models remain task-specific, annotation-dependent, and limited in generalizability across diseases and anatomical regions. Although a generalizable foundation model trained on large-scale musculoskeletal radiographs is clinically needed, publicly available datasets remain limited in size and lack sufficient diversity to enable training across a wide range of musculoskeletal conditions and anatomical sites. Here, we present SKELEX, a large-scale foundation model for musculoskeletal radiographs, trained using self-supervised learning on 1.2 million diverse, condition-rich images. The model was evaluated on 12 downstream diagnostic tasks and generally outperformed baselines in fracture detection, osteoarthritis grading, and bone tumor classification. Furthermore, SKELEX demonstrated zero-shot abnormality localization, producing error maps that identified pathologic regions without task-specific training. Building on this capability, we developed an interpretable, region-guided model for predicting bone tumors, which maintained robust performance on independent external datasets and was deployed as a publicly accessible web application. Overall, SKELEX provides a scalable, label-efficient, and generalizable AI framework for musculoskeletal imaging, establishing a foundation for both clinical translation and data-efficient research in musculoskeletal radiology.

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