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.

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.

Static Postural Control Deficits in Adults with Myotonic Dystrophy Type 1, Steinert Disease.

BACKGROUND: Myotonic dystrophy type 1 (DM1) is characterized by progressive and predominantly distal muscle atrophy and myotonia. Gait and balance impairments, resulting in falls, are frequently reported in this population. However, the extent to which individuals with DM1 rely more on a specific sensory system for balance than asymptomatic individuals (AI) is unknown. OBJECTIVE: Evaluate postural control performance in individuals with DM1 and its dependence on vision compared to AI. METHODS: 20 participants with DM1, divided into two groups based on their diagnosis, i.e. adult and congenital phenotype, and 12 AI participants were recruited. Quiet standing postural control was assessed in two visual conditions: eyes-open and eyes-closed. The outcomes measures were: center of pressure (CoP), mean velocity, CoP range of displacement in anteroposterior and mediolateral axis, and the 95% confidence ellipse's surface. Friedman and Kruskal-Wallis analysis of variance were used to compare outcomes between conditions and groups, respectively. RESULTS: Significant group effect and condition effect were observed on postural control performance. No significant difference was observed between the two DM1 groups. The significant differences observed between the AI group and the two DM1 groups in the eyes-open condition were also observed in the eyes-closed condition. CONCLUSIONS: The result revealed poorer postural control performance in people with DM1 compared to AI. The DM1 group also showed similar decrease in performance than AI in eyes-closed condition, suggesting no excessive visual dependency.