  Anterior Vertebral Body Growth Modulation (AVBGM) is a minimally invasive
surgical technique that gradually corrects spine deformities while preserving
lumbar motion. However the selection of potential surgical patients is
currently based on clinical judgment and would be facilitated by the
identification of patients responding to AVBGM prior to surgery. 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 exams, 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 exams, with the probabilistic framework yielding accurate results
with differences of 2.1 +/- 0.6deg in main curve angulation, and generating
models similar to biomechanical simulations.
