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.
This study was Supported by NSERC, CHU Sainte-Justine Academic Research Chair in Spinal Deformities, the Canada Research Chair in Medical Imaging and Assisted Interventions.
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Mandel, W., Korez, R., Nault, ML., Parent, S., Kadoury, S. (2016). Classification of Progressive and Non-progressive Scoliosis Patients Using Discriminant Manifolds. In: Yao, J., Vrtovec, T., Zheng, G., Frangi, A., Glocker, B., Li, S. (eds) Computational Methods and Clinical Applications for Spine Imaging. CSI 2016. Lecture Notes in Computer Science(), vol 10182. Springer, Cham. https://doi.org/10.1007/978-3-319-55050-3_13
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DOI: https://doi.org/10.1007/978-3-319-55050-3_13
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