Edgar García-Cano1, Fernando Arámbula Cosío2, Luc Duong3, Christian Bellefleur4, Marjolaine Roy-Beaudry4, Julie Joncas4, Stefan Parent4, Hubert Labelle4. 1. École de technologie supérieure, Université du Québec, 1100 Notre-Dame Street West, Montreal, Quebec, H3C 1K3, Canada. Electronic address: gcano.edgar@gmail.com. 2. Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Circuito Escolar S/N, Ciudad Universitaria, México D.F, C.P. 04510, Mexico. 3. École de technologie supérieure, Université du Québec, 1100 Notre-Dame Street West, Montreal, Quebec, H3C 1K3, Canada. 4. Research Center, Sainte-Justine Hospital, 3175 Côte-Sainte-Catherine, Montreal, Quebec, Canada.
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.
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.
Authors: Alexander L Hornung; Christopher M Hornung; G Michael Mallow; J Nicolás Barajas; Augustus Rush; Arash J Sayari; Fabio Galbusera; Hans-Joachim Wilke; Matthew Colman; Frank M Phillips; Howard S An; Dino Samartzis Journal: Eur Spine J Date: 2022-03-27 Impact factor: 2.721
Authors: Cesare Faldini; Marco Manzetti; Simona Neri; Francesca Barile; Giovanni Viroli; Giuseppe Geraci; Francesco Ursini; Alberto Ruffilli Journal: Int J Mol Sci Date: 2022-05-25 Impact factor: 6.208