Classification of progressive and non-progressive scoliosis patients using discriminant manifolds

Abstract

Adolescent idiopathic scoliosis (AIS) is a 3-D deformation of the spine. Identifying curve progression in AIS at the first visit is a clinically relevant problem but remains challenging due to lack of relevant descriptors. We present here a classification framework to identify patients whose spine deformity will progress from those who will remain stable. The method uses personalized 3-D spine reconstructions at baseline from progressive (P) and non-progressive (NP) patients to train a predictive model. Morphological changes between groups are detected using a manifold learning algorithm based on Grassmannian kernels in order to assess the similarity between shape topology and inter-vertebral poses in both groups (P, NP). We test the method to classify 52 progressive and 81 non-progressive patients enrolled in a prospective clinical study, yielding classification rates comparing favorably to standard classification methods.