Advanced Analytic Modeling for Growth and Curve Progression in Idiopathic Scoliosis
This project develops machine learning models to better understand and predict growth patterns and curve progression in pediatric patients with idiopathic scoliosis. Using historical data such as height measurements, radiological maturity scores, and CXM, a collagen X biomarker identified by the Shriners team, the project aims to uncover how different features contribute to patient growth and Cobb angle progression.
Project Overview
The Challenge
Predicting growth cessation and curve progression in idiopathic scoliosis is clinically important but complex. Clinicians must consider biological maturity, radiographic measures, biomarkers, and longitudinal patient data when making treatment decisions, including monitoring and bracing strategies.
The Innovation
The project applies advanced analytic and machine learning models to time-series clinical and radiographic data. By incorporating CXM and other maturity measures, the work aims to improve prediction of growth cessation and curve progression beyond traditional assessment methods.
Objective
To develop machine learning models that predict growth, growth cessation, and Cobb angle progression in children with idiopathic scoliosis.
Current Phase
Active/in progress. The report lists submitted presentations to the Scoliosis Research Society Annual Meeting 2025 focused on predictors of curve magnitude change and CXM as a predictor of growth cessation.
Potential Impact
This work could help clinicians better predict scoliosis progression, personalize monitoring and treatment strategies, and improve timing of interventions for pediatric patients.
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Interested in collaborating or supporting this work?
We welcome clinical partners, research collaborators, and supporters who share a commitment to advancing pediatric innovation. Reach out to connect with the project team or explore related work across GTPIN.