Development of Machine learning algorithms to identify the Cobb angle in adolescents with idiopathic scoliosis based on lumbosacral joint efforts during gait (Case study)

TL;DR

This study develops a machine learning-based, radiation-free method to estimate the Cobb angle in adolescents with scoliosis by analyzing gait-related lumbosacral joint efforts, offering a safer alternative to X-ray imaging.

Contribution

The paper introduces a novel approach using gait analysis and machine learning to accurately predict Cobb angles, reducing reliance on X-ray imaging for scoliosis monitoring.

Findings

Decision tree regression achieved 4.6° mean absolute error.

Gait-based features can effectively estimate Cobb angles.

Radiation-free method shows promise for clinical follow-up.

Abstract

Objectives: To quantify the magnitude of spinal deformity in adolescent idiopathic scoliosis (AIS), the Cobb angle is measured on X-ray images of the spine. Continuous exposure to X-ray radiation to follow-up the progression of scoliosis may lead to negative side effects on patients. Furthermore, manual measurement of the Cobb angle could lead to up to 10{\deg} or more of a difference due to intra/inter observer variation. Therefore, the objective of this study is to identify the Cobb angle by developing an automated radiation-free model, using Machine learning algorithms. Methods: Thirty participants with lumbar/thoracolumbar AIS (15{\deg} < Cobb angle < 66{\deg}) performed gait cycles. The lumbosacral (L5-S1) joint efforts during six gait cycles of participants were used as features to feed training algorithms. Various regression algorithms were implemented and run. Results: The decision tree regression algorithm achieved the best result with the mean absolute error equal to 4.6{\deg} of averaged 10-fold cross-validation. Conclusions: This study shows that the lumbosacral joint efforts during gait as radiation-free data are capable to identify the Cobb angle by using Machine learning algorithms. The proposed model can be considered as an alternative, radiation-free method to X-ray radiography to assist clinicians in following-up the progression of AIS.