Interpretable Source-Aware Bone-Muscle Fusion Framework for Multicenter Prediction of Osteoporotic Vertebral Fractures.
Authors
Affiliations (14)
Affiliations (14)
- School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, Guangxi, China.
- Jiangmen Key Laboratory of Artificial Intelligence in Medical Image Computation and Application, Jiangmen Central Hospital, Jiangmen, Guangdong, China.
- Department of Radiology, Guangzhou First People's Hospital, No. 1 Panfu Road, Yuexiu District, Guangzhou, Guangdong, China.
- Department of Spinal Surgery, Baishi Orthopedic Hospital, 66 Baishi Road, Pengjiang District, Jiangmen, Guangdong, China.
- Department of Spinal Surgery, The Fifth Affiliated Hospital, Sun Yat-Sen University, 52 East Meihua Road, Xiangzhou District, Zhuhai, Guangdong, China.
- Department of Surgery of Joint and Extremities Osteopathy, Maoming People's Hospital, Maoming, Guangdong, China.
- Department of Spinal Surgery, Jiangmen Central Hospital, 23 Haibang Road, Pengjiang District, Jiangmen, Guangdong, China.
- Laboratory of Artificial Intelligence of Biomedicine, Guilin University of Aerospace Technology, Guilin, Guangxi, China.
- Department of Radiology, Jiangmen Central Hospital, 23 Haibang Road, Pengjiang District, Jiangmen, Guangdong, China.
- School of Mathematical Sciences, Guangxi Minzu University, Nanning, China.
- Jiangmen Key Laboratory of Artificial Intelligence in Medical Image Computation and Application, Jiangmen Central Hospital, Jiangmen, Guangdong, China. [email protected].
- Laboratory of Artificial Intelligence of Biomedicine, Guilin University of Aerospace Technology, Guilin, Guangxi, China. [email protected].
- Laboratory of Artificial Intelligence of Biomedicine, Guilin University of Aerospace Technology, Guilin, Guangxi, China. [email protected].
- Jiangmen Key Laboratory of Artificial Intelligence in Medical Image Computation and Application, Jiangmen Central Hospital, Jiangmen, Guangdong, China. [email protected].
Abstract
Osteoporotic vertebral fracture (OVF) is underrecognized, and bone mineral density (BMD) alone may be insufficient for prediction. We developed the multisource fusion model for osteoporotic vertebral fracture (MSF-OVF), an interpretable source-aware framework integrating clinical-demographic variables, BMD, and lumbar MRI-derived ventral and dorsal paraspinal muscle (PM) representations. This retrospective multicenter study included 2680 participants aged > 40 years from five centers (one training, one internal validation, four external test cohorts). Deep features from four PM regions were extracted using vision foundation model-guided transfer learning and integrated with age, sex, body mass index, and BMD through source-aware latent fusion. Performance was evaluated using the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), calibration, and interpretability analyses. MSF-OVF achieved an AUC of 0.881 (95% CI, 0.854-0.909) in internal validation and 0.851-0.917 across four external cohorts. Compared with the clinical-demographic and BMD baseline, MSF-OVF improved AUC by 0.046-0.093 and significantly outperformed it in internal validation and most external cohorts (DeLong P ≤ 0.034). Continuous NRI was 0.268-1.034 across all validation cohorts (P ≤ 0.011). MSF-OVF also outperformed single-muscle models (AUC, 0.454-0.736; P ≤ 0.009). After training-derived logistic recalibration, the internal validation Brier score was 0.140, with a calibration-in-the-large (CITL) of - 0.168 and calibration slope of 0.926. Class activation maps mainly highlighted PM regions. Automated segmentation took several seconds and inference 57.71 ± 18.66 ms per participant. Source-aware fusion of clinical-demographic, BMD, and PM MRI information may support computer-aided multicenter OVF prediction.