Machine Learning Prediction of Disease Progression in Adolescent 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

Overview

Background and Problem Overview Adolescent Idiopathic Scoliosis (AIS) is a progressive musculoskeletal disorder characterized by a three-dimensional deformation of the spine occurring during adolescence. Diagnosis is typically established with a Cobb angle exceeding 10° and the presence of axial rotation. While the exact etiology remains unknown, leading theories include tissue abnormalities (muscle fibers, bone volume), impaired spinal biomechanics (asymmetric bone growth), and neurological factors (asymmetric cortical thickness, cerebral lateralization, and body schema distortions). The progressive nature of AIS, particularly the high risk of advancement at the onset of puberty, complicates clinical decision-making. Treatment is traditionally divided into three stages: Observation and Exercise: For Cobb angles between 10°-25°. Exercise and Bracing: For Cobb angles between 25°-45°. Surgery: For Cobb angles exceeding 45°. Despite these guidelines, the unpredictable progression of the disease and difficulties in treatment adherence create significant dilemmas. Specifically, for cases on the borderline of surgical indication, clinicians face the challenge of choosing between immediate surgery or conservative monitoring. Currently, there is no definitive method to predict progression, and patients are typically monitored in 6-month intervals. During these intervals, a patient's condition may remain stable or deteriorate significantly. Furthermore, guidelines recommend wearing a brace for an average of 18 hours per day, often for several years. This requirement is physically and psychologically demanding for adolescents, leading to poor compliance due to aesthetic concerns, functional limitations, and skin irritation. The inability to predict progression often leads to overtreatment (unnecessary bracing) or undertreatment (delayed intervention), both of which pose risks to the patient's long-term health. Radiological Concerns Disease progression is monitored via direct radiography (X-rays). However, frequent imaging increases the lifetime risk of cancer due to cumulative ionizing radiation. Notably, the risk of breast cancer in girls with AIS is reported to be approximately seven times higher than in the healthy population. Conversely, extending follow-up intervals risks missing windows for early intervention. An artificial intelligence (AI) model capable of predicting curve progression could optimize imaging frequency, ensuring safety while maintaining clinical efficacy. Objective and Methodology of the Study The primary aim of this research is to develop a machine learning-based model to predict the Cobb angle following a 12-week exercise intervention. The model will utilize comprehensive baseline and post-treatment data, including: Demographic and Anthropometric Data (Age, height, weight, gender). Clinical Assessments (Cobb angle, Risser score, angle of trunk rotation). Functional and Physical Metrics (Trunk muscle strength, Maximal Inspiratory and Expiratory Pressure \[MIP/MEP\], Biodex balance measurements). Visual Assessments (Walter Reed Visual Deformity Scale \[WRVAS\]). Research Hypotheses Primary Hypothesis: A machine learning model trained on pre- and post-exercise assessment data can significantly predict the Cobb angle at the end of a 12-week period with both statistical and clinical accuracy. Secondary Hypothesis: By predicting the risk of progression (the probability of an increase in Cobb angle), this model will contribute to reducing unnecessary surgical interventions, overtreatment (bracing/surgery), and cumulative X-ray exposure.

Interventions

• Behavioral Core Stabilization Exercise
  weeks. The exercise program focuses on improving trunk muscle control, postural stability, and spinal alignment. The intervention is delivered as part of routine physiotherapy care. Participants perform exercises targeting deep trunk stabilizers, including abdominal, paraspinal, and pelvic musculature. Exercise progression is based on patient tolerance and clinical evaluation. Clinical and radiological assessments are performed before and after the intervention, including Cobb angle measuremen

Primary outcome measures

• Cobb Angle [Time frame: 12 weeks]

Secondary outcome measures (6)

• Demographic Parameters [Time frame: 12 weeks]
• Risser Score [Time frame: 12 weeks]
• Angle of Trunk Rotation [Time frame: 12 weeks]
• The Walter Reed Visual Assessment Scale [Time frame: 12 weeks]
• Biodex postural stability and limits of stability [Time frame: 12 weeks]
• Respiratory Pressure [Time frame: 12 weeks]

Eligibility 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

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Allocation

N/A

Model

Single group

Masking

Open label

Primary purpose

Treatment

Study locations

Turkey (Türkiye) · 1 center

• Bezmialem Vakif University — Istanbul

Identifiers

NCT: NCT07556042 · IUFGokdemir03

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗