Opportunistic Osteoporosis Diagnosis via Texture-Preserving Self-Supervision, Mixture of Experts and Multi-Task Integration

TL;DR

This paper introduces a comprehensive deep learning framework for opportunistic osteoporosis diagnosis using CT scans, leveraging self-supervision, mixture of experts, and multi-task learning to improve accuracy and generalizability across different clinical settings.

Contribution

It presents a novel unified approach combining self-supervised learning, MoE architecture, and multi-task integration to address key limitations in opportunistic osteoporosis diagnosis.

Findings

Outperforms existing methods in accuracy and generalizability

Effectively leverages unlabeled CT data through self-supervision

Demonstrates robustness across multiple clinical sites

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.