Multi-task learning for classification and prediction of adolescent idiopathic scoliosis based on fringe-projection three-dimensional imaging.
Authors
Affiliations (4)
Affiliations (4)
- Department of Orthopaedics and Traumatology, Taipei Veterans General Hospital, Taipei, Taiwan.
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan.
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan. [email protected].
- Medical Device Innovation and Translation Center, National Yang Ming Chiao Tung University, Taipei, Taiwan. [email protected].
Abstract
This study aims to address the limitations of radiographic imaging and single-task learning models in adolescent idiopathic scoliosis assessment by developing a noninvasive, radiation-free diagnostic framework. A multi-task deep learning model was trained using structured back surface data acquired via fringe projection three-dimensional imaging. The model was designed to simultaneously predict the Cobb angle, curve type (thoracic, lumbar, mixed, none), and curve direction (left, right, none) by learning shared morphological features. The multi-task model achieved a mean absolute error (MAE) of 2.9° and a root mean square error (RMSE) of 6.9° for Cobb angle prediction, outperforming the single-task baseline (5.4° MAE, 12.5° RMSE). It showed strong correlation with radiographic measurements (R = 0.96, R² = 0.91). For curve classification, it reached 89% sensitivity in lumbar and mixed types, and 80% and 75% sensitivity for right and left directions, respectively, with an 87% positive predictive value for right-sided curves. The proposed multi-task learning model demonstrates that jointly learning related clinical tasks allows for the extraction of more robust and clinically meaningful geometric features from surface data. It outperforms traditional single-task approaches in both accuracy and stability. This framework provides a safe, efficient, and non-invasive alternative to X-ray-based scoliosis assessment and has the potential to support real-time screening and long-term monitoring of adolescent idiopathic scoliosis in clinical practice.