Machine-learning prediction of curve progression and core stabilization exercises for adolescents with idiopathic scoliosis.
Official title: Machine Learning-Based Prediction of Disease Progression in Adolescent Idiopathic Scoliosis Following Core Stabilization Exercise: A Retrospective Model Development and Prospective Validation Study
Run by Istanbul University · NCT07556042 · Data updated 2026-11-12
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
| Study type | Interventional |
|---|---|
| Ages | 10 Years to 18 Years |
| Sex | All |
| Enrollment | 30 (estimated) |
| Sponsor | Istanbul University Academic / other |
| Drugs / interventions | radiation |
| Locations | 1 site (Istanbul) |
| Status | Recruiting |
| Started | April 20, 2026 |
| Main results expected | September 10, 2026 |
| Study ends | September 12, 2026 |
| Registry record updated | August 19, 2026 |
| Trial ID | NCT07556042 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a 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.
✕Probably 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.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
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.
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 gone: 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.
Who can join: the full rules
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 it's running
-
Bezmialem Vakif University
Istanbul, 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
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.