A preliminary study in classification of the severity of spine deformation in adolescents with lumbar/thoracolumbar idiopathic scoliosis using machine learning algorithms based on lumbosacral joint efforts during gait

Abstract To assess the severity and progression of adolescents with idiopathic scoliosis (AIS), radiography with X-rays is usually used. The methods based on statistical observations have been developed from 3D reconstruction of the trunk or topography. Machine learning has shown great potential to classify the severity of scoliosis on imaging data, generally on X-ray measurements. It is also known that AIS leads to the development of gait disorder. To our knowledge, machine learning has never been tested on spine intervertebral efforts during gait as a radiation-free method to classify the severity of spinal deformity in AIS. Develop automated machine learning algorithms in lumbar/thoracolumbar scoliosis to classify the severity of spinal deformity of AIS based on the lumbosacral joint (L5-S1) efforts during gait. The lumbosacral joint efforts of 30 individuals with lumbar/thoracolumbar AIS were used as distinctive features fed to the machine learning algorithms. Several tests were run using various classification algorithms. The labeling consisted of three classes reflecting the severity of scoliosis i.e. mild, moderate and severe. The ensemble classifier algorithm including k-nearest neighbors, support vector machine, random forest and multilayer perceptron achieved the most promising results, with accuracy scores of 91.4%. This preliminary study shows lumbosacral joint efforts can be used to classify the severity of spinal deformity in lumbar/thoracolumbar AIS. This method showed the potential of being used as an assessment tool to follow-up the progression of AIS as a radiation-free method, alternative to radiography. Future studies should be performed to test the method on other categories of AIS.