ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2025
Multi-task learning for classification and prediction of adolescent idiopathic scoliosis based on fringe-projection three-dimensional imaging.
Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Multimodal quantitative characterization and mechanistic exploration of paraspinal muscle groups in adolescent idiopathic scoliosis.Frontiers in pediatrics · 2026Article
- Article
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5 authors.
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Abstract
purposeThis 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.
methodsA 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.
resultsThe 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.
conclusionThe 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.
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