Opportunistic Osteoporosis Diagnosis via Texture-Preserving Self-Supervision, Mixture of Experts and Multi-Task Integration
Journal:
arXiv
Published Date:
Jun 25, 2025
Abstract
Osteoporosis, characterized by reduced bone mineral density (BMD) and
compromised bone microstructure, increases fracture risk in aging populations.
While dual-energy X-ray absorptiometry (DXA) is the clinical standard for BMD
assessment, its limited accessibility hinders diagnosis in resource-limited
regions. Opportunistic computed tomography (CT) analysis has emerged as a
promising alternative for osteoporosis diagnosis using existing imaging data.
Current approaches, however, face three limitations: (1) underutilization of
unlabeled vertebral data, (2) systematic bias from device-specific DXA
discrepancies, and (3) insufficient integration of clinical knowledge such as
spatial BMD distribution patterns. To address these, we propose a unified deep
learning framework with three innovations. First, a self-supervised learning
method using radiomic representations to leverage unlabeled CT data and
preserve bone texture. Second, a Mixture of Experts (MoE) architecture with
learned gating mechanisms to enhance cross-device adaptability. Third, a
multi-task learning framework integrating osteoporosis diagnosis, BMD
regression, and vertebra location prediction. Validated across three clinical
sites and an external hospital, our approach demonstrates superior
generalizability and accuracy over existing methods for opportunistic
osteoporosis screening and diagnosis.
