Prediction outcomes for anterior vertebral body growth modulation surgery from discriminant spatiotemporal manifolds.

Abstract

Anterior vertebral body growth modulation (AVBGM) is a minimally invasive surgical technique that gradually corrects spine deformities while preserving lumbar motion. However, identifying suitable patients for surgery is based on clinical judgment and surgical experience. This process would be facilitated by the identification of patients responding to AVBGM prior to surgery using data-driven models trained on previous instrumented cases. We introduce a statistical framework for predicting the surgical outcomes following AVBGM in adolescents with idiopathic scoliosis. A discriminant manifold is first constructed to maximize the separation between responsive and non-responsive groups of patients treated with AVBGM for scoliosis. The model then uses subject-specific correction trajectories based on articulated transformations in order to map spine correction profiles to a group-average piecewise-geodesic path. Spine correction trajectories are described in a piecewise-geodesic fashion to account for varying times at follow-up examinations, regressing the curve via a quadratic optimization process. To predict the evolution of correction, a baseline reconstruction is projected onto the manifold, from which a spatiotemporal regression model is built from parallel transport curves inferred from neighboring exemplars. The model was trained on 438 reconstructions and tested on 56 subjects using 3D spine reconstructions from follow-up examinations, with the probabilistic framework yielding accurate results with differences of [Formula: see text] in main curve angulation and a classification rate of 83.2%, and generating models similar to biomechanical simulations. The proposed method achieved a higher prediction accuracy and improved the modeling of spatiotemporal morphological changes in surgical patients treated with AVBGM.