A generalizable large-scale foundation model for musculoskeletal radiographs

TL;DR

SKELEX is a large-scale, self-supervised foundation model for musculoskeletal radiographs that generalizes across multiple diagnostic tasks, demonstrates zero-shot localization, and supports clinical and research applications.

Contribution

The paper introduces SKELEX, a novel large-scale foundation model trained on 1.2 million images, enabling broad generalization and zero-shot localization in musculoskeletal radiology.

Findings

Outperforms baselines in fracture detection, osteoarthritis grading, and bone tumor classification.

Demonstrates zero-shot abnormality localization with error maps.

Maintains robust performance on external datasets and is publicly accessible.

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.