Image-Guided Tethering Spine Surgery With Outcome Prediction Using Spatio-Temporal Dynamic Networks

Recent fusionless surgical techniques for corrective spine surgery such as Anterior Vertebral Body Growth Modulation (AVBGM) allow to treat mild to severe spinal deformations by tethering vertebral bodies together, helping to preserve lower back flexibility. Forecasting the outcome of AVBGM from skeletally immature patients remains elusive with several factors involved in corrective vertebral tethering, but could help orthopaedic surgeons plan and tailor AVBGM procedures prior to surgery. We introduce an intra-operative framework forecasting the outcomes during AVBGM surgery in scoliosis patients. The method is based on spatial-temporal corrective networks, which learns the similarity in segmental corrections between patients and integrates a long-term shifting mechanism designed to cope with timing differences in onset to surgery dates, between patients in the training set. The model captures dynamic geometric dependencies in scoliosis patients, ensuring long-term dependency with temporal dynamics in curve evolution and integrated features from inter-vertebral disks extracted from T2-w MRI. The loss function of the network introduces a regularization term based on learned group-average piecewise-geodesic path to ensure the generated corrective transformations are coherent with regards to the observed evolution of spine corrections at follow-up exams. The network was trained on 695 3D spine models and tested on 72 operative patients using a set of 3D spine reconstructions as inputs. The spatio-temporal network predicted outputs with errors of 1.8 +/- 0.8mm in 3D anatomical landmarks, yielding geometries similar to ground-truth spine reconstructions obtained at one and two year follow-ups and with significant improvements to comparative deep learning and biomechanical models.