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Objectives: To quantify the magnitude of spinal deformity in adolescentidiopathic scoliosis (AIS), the Cobb angle is measured on X-ray images of thespine. Continuous exposure to X-ray radiation to follow-up the progression ofscoliosis may lead to negative side effects on patients. Furthermore, manualmeasurement of the Cobb angle could lead to up to 10{\deg} or more of adifference due to intra/inter observer variation. Therefore, the objective ofthis study is to identify the Cobb angle by developing an automatedradiation-free model, using Machine learning algorithms. Methods: Thirtyparticipants with lumbar/thoracolumbar AIS (15{\deg} < Cobb angle < 66{\deg})performed gait cycles. The lumbosacral (L5-S1) joint efforts during six gaitcycles of participants were used as features to feed training algorithms.Various regression algorithms were implemented and run. Results: The decisiontree regression algorithm achieved the best result with the mean absolute errorequal to 4.6{\deg} of averaged 10-fold cross-validation. Conclusions: Thisstudy shows that the lumbosacral joint efforts during gait as radiation-freedata are capable to identify the Cobb angle by using Machine learningalgorithms. The proposed model can be considered as an alternative,radiation-free method to X-ray radiography to assist clinicians in following-upthe progression of AIS.

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