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

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° 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° < Cobb angle < 66°) 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° 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.

Uncontrolled Keywords

• radiation-free
• Cobb angle
• machine learning regression models
• adolescent idiopathic scoliosis
• intervertebral efforts