ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2026
Advanced machine learning model for Cobb angle progression in adolescent idiopathic scoliosis with surface topography: a multicenter, prospective, observational study.
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, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
introductionExcessive radiographic exposure in the follow-up of adolescent idiopathic scoliosis (AIS) remains a clinical concern. Surface topography (ST) and angle of trunk rotation (ATR) have shown promise for non-radiographic monitoring, although their ability to detect clinically meaningful Cobb angle changes (> 5°) remains limited. This study aimed to validate a machine learning model for predicting Cobb angle progression using ST parameters and ATR obtained at three and six months.
methodsA prospective observational study was conducted in 43 AIS patients (57 curves) recruited from two centers. Baseline and six-month radiographic Cobb angles were recorded along with ATR and five ST asymmetry parameters (MaxDev, RMS, LatDev, hump volume, asymmetry patch area). A random forest (RF) model was used to predict Cobb angles at 3 and 6 months and then to estimate progression over 6 months (ΔCobb). Outcomes were classified as improvement (ΔCobb < -5°), stabilization (-5° ≤ ΔCobb ≤ + 5°), or progression (ΔCobb > + 5°).
resultsThe RF model predicted the six-month radiographic Cobb with MAE of 7.03° (three-month input) and 6.91° (six-month input). The progression model integrating both time points achieved an overall accuracy of 80.7%, with detection accuracies of 100% for stabilization, 53.8% for improvement, and 37.5% for progression. Quantitatively, 84.2% of the curves had a progression prediction error of less than 5°.
conclusionThe model accurately identified stabilized cases, suggesting that non-radiographic follow-up combining ST and ATR could reliably detect non-progressive AIS within six months. This approach could potentially reduce up to 80% of unnecessary follow-up radiographs in stable patients.
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