aceDevelopment and Validation of an Interpretable Machine Learning Model for Osteoporosis Prediction Using Multimodal Musculoskeletal Ultrasound and Clinical Features
This study aimed to develop and validate an interpretable machine learning model for osteoporosis prediction based on multimodal ultrasound and clinical features. Participants with suspected osteoporosis were enrolled from 2 medical centers. Clinical variables and multimodal musculoskeletal ultrasound parameters, including muscle thickness, cross-sectional area, and shear wave elastography measurements, were collected. LASSO regression was used for feature selection. Eight machine learning algorithms, including…
An interpretable XGBoost model combining clinical variables and multimodal musculoskeletal ultrasound achieved high accuracy for osteoporosis prediction, offering a practical tool for early risk stratification.
Evidence
- Peer-reviewedJournal of Ultrasound in Medicine2026-10-04
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Truvace Impact Record TRV-2026-1323, v1: “Development and Validation of an Interpretable Machine Learning Model for Osteoporosis Prediction Using Multimodal Musculoskeletal Ultrasound and Clinical Features.” Truvace, 2026-10-11. /record/TRV-2026-1323 (accessed at citation time). sha256 52f48dbe75352b1d…
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