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Introduction <p>Excessive 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 (&gt; 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.</p> Methods <p>A 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 &lt; -5°), stabilization (-5° ≤ ΔCobb ≤ + 5°), or progression (ΔCobb &gt; + 5°).</p> Results <p>The 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°.</p> Conclusion <p>The 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.</p>

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Advanced machine learning model for Cobb angle progression in adolescent idiopathic scoliosis with surface topography: a multicenter, prospective, observational study

  • 期刊论文

  • 发表日期：

    2026年5月7日

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  • José María González Ruiz,
  • Judith Salat-Batlle,
  • Macarena Alejandra Rodas Rivas,
  • Judith Sánchez-Raya,
  • Joan Bagó,
  • Joan Masnou,
  • Pamela Andrea Espinoza Poblete,
  • Marco Morillo Armendariz,
  • Susana Núñez-Pereira,
  • Bruna Nichele da Rosa,
  • Zeinab Estaji,
  • Lindsey Westover

摘要

Introduction

Excessive 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.

Methods

A 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°).

Results

The 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°.

Conclusion

The 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.