Machine-learning prediction of curve progression and core stabilization exercises for adolescents with idiopathic scoliosis.
Machine Learning-Based Prediction of Disease Progression in Adolescent Idiopathic Scoliosis Following Core Stabilization Exercise: A Retrospective Model Development and Prospective Validation Study
This project will test whether a machine-learning tool can predict curve progression and whether a course of core stabilization exercises helps adolescents aged 10–18 with idiopathic scoliosis.
Quick facts
| Phase | Not applicable |
|---|---|
| Study type | Interventional |
| Enrollment | 30 (estimated) |
| Ages | 10 Years to 18 Years |
| Sex | All |
| Sponsor | Istanbul University Academic / other |
| Drugs / interventions | radiation |
| Locations | 1 site (Istanbul, Eyupsultan) |
| Trial ID | NCT07556042 on ClinicalTrials.gov |
What this trial studies
This interventional protocol combines a core stabilization exercise program with a machine-learning approach to predict disease progression in adolescents with idiopathic scoliosis. Participants aged 10–18 with Cobb angles of 10–40° who are not receiving other scoliosis-specific exercise treatment will be enrolled and followed at a single center in Istanbul. Clinical and imaging measures (including Cobb angle and axial rotation) will be tracked over time and used to train and validate predictive models while patients complete the exercise intervention. Patients with prior recent surgery or comorbid conditions that preclude exercise are excluded.
Who should consider this trial
Good fit: Ideal candidates are adolescents aged 10–18 with idiopathic scoliosis and Cobb angles between 10° and 40° who are not currently receiving other scoliosis-specific exercise treatment.
Not a fit: Patients with Cobb angles under 10° or over 40°, prior spinal surgery, recent surgery within three months, or medical/neurological conditions that prevent exercise are unlikely to benefit from this protocol.
Why it matters
Potential benefit: If successful, the approach could allow earlier, more personalized decisions about monitoring, bracing, or exercise and potentially slow curve progression for some patients.
How similar studies have performed: Exercise programs for AIS have shown mixed but sometimes positive effects on progression, and emerging machine-learning models have shown promise for predicting curve progression, though combining them is relatively novel.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * being between the ages of 10 and 18 * having a Cobb angle between 10 and 40 degrees * not receiving any other exercise treatment (scoliosis-specific exercises, etc.) from a different center that would affect the patient's scoliosis Exclusion Criteria: * history of scoliosis surgery * patients who had undergone any type of surgical procedure within the last 3 months were excluded * orthopedic, neurological, or systemic diseases that would hinder exercise * Intellectual, behavioral, or communication disorders affecting understanding of instructions or exercise performance, or participation in any exercise
Where this trial is running
Istanbul, Eyupsultan
- Bezmialem Vakif University — Istanbul, Eyupsultan, Turkey (Türkiye) (Recruiting)
Study contacts
- Principal investigator: Fuat Gökdemir — Bezmialem Vakif University
- Study coordinator: Fuat Gökdemir
- Email: fuatgokdemir95@gmail.com
- Phone: +90 212 401 26 00
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.