Prediction of spinal curve progression in Adolescent Idiopathic Scoliosis using Random Forest regression. (1st December 2018)

• Record Type: Journal Article
• Authors: García-Cano, Edgar; Arámbula Cosío, Fernando; Duong, Luc; Bellefleur, Christian; Roy-Beaudry, Marjolaine; Joncas, Julie; Parent, Stefan; Labelle, Hubert
• Is Part Of: Computers in biology and medicine. Volume 103(2018)
• Page Start: 34
• Page End: 43
• Publication Date: 2018-12-01
• Subjects: Prediction of spinal curve progression -- Adolescent idiopathic scoliosis -- Independent component analysis -- Machine learning -- Random forest; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2018.09.029
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background: The progression of the spinal curve represents one of the major concerns in the assessment of Adolescent Idiopathic Scoliosis (AIS). The prediction of the shape of the spine from the first visit could guide the management of AIS and provide the right treatment to prevent curve progression. Method: In this work, we propose a novel approach based on a statistical generative model to predict the shape variation of the spinal curve from the first visit. A spinal curve progression approach is learned using 3D spine models generated from retrospective biplanar X-rays. The prediction is performed every three months from the first visit, for a time lapse of one year and a half. An Independent Component Analysis (ICA) was computed to obtain Independent Components (ICs), which are used to describe the main directions of shape variations. A dataset of 3D shapes of 150 patients with AIS was employed to extract the ICs, which were used to train our approach. Results: The approach generated an estimation of the shape of the spine through time. The estimated shape differs from the real curvature by 1.83, 5.18, and 4.79° of Cobb angles in the proximal thoracic, main thoracic, and thoraco-lumbar lumbar sections, respectively. Conclusions: The results obtained from our approach indicate that predictions based on ICs are very promising. ICA offers the means to identify the variation in the 3D space of the evolution of the shape of the spine. Another advantage of using ICs is that they can be visualized for interpretation. Highlights: Morphologic descriptors could help characterize changes in the spine in 3D space. Using independent components, we predicted the evolution of the spine's shape. The predictor models the changes in the spine up to 18 months from first visit. Prediction of the shape of the spine could help devise patient-specific treatment. Future research may detect curve patterns signaling progressive idiopathic scoliosis.