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Prediction of the upright articulated spine shape in the operating room using conditioned neural kernel fields. | LitMetric

AI Article Synopsis

  • Anterior vertebral tethering (AVT) is a non-invasive technique for spine surgery that helps treat severe spinal deformities while maintaining mobility in the lower back.
  • Patient positioning during surgery significantly affects recovery, making it crucial to predict the upright geometry of pediatric spines, despite challenges arising from underdeveloped bone structure.
  • A new framework using advanced imaging and neural networks successfully predicts the postoperative spine shape in idiopathic scoliosis patients, achieving an impressive accuracy with an average error of just 1.3 mm in 3D landmark points.

Article Abstract

Anterior vertebral tethering (AVT) is a non-invasive spine surgery technique, treating severe spine deformations and preserving lower back mobility. However, patient positioning and surgical strategies greatly influences postoperative results. Predicting the upright geometry from pediatric spines is needed to optimize patient positioning in the operating room (OR) and improve surgical outcomes, but remains a complex task due to immature bone properties. We propose a framework used in the OR predicting the upright spine geometry at the first visit following surgery in idiopathic scoliosis patients. The approach first creates a 3D model of the spine while the patient is on the operating table. For this, multiview Transformers that combine images from different viewpoints are used to generate the intraoperative pose. The postoperative upright shape is then predicted on-the-fly using implicit neural fields, which are trained from geometries at different time points and conditioned with surgical parameters. A Signed Distance Function for shape constellations is used to handle the variability in spine appearance, capturing a disentangled latent domain of the articulation vectors, with separate encoding vectors representing both articulation and shape parameters. A regularization criterion based on a pre-trained group-wise trajectory of spine transformations generates complete spine models. A training set of 652 patients with 3D models was used to train the model, tested on a distinct cohort of 83 surgical patients. The framework based on neural kernels predicted upright 3D geometries with a mean 3D error of 1.3±0.5mm in landmarks points, and IoU of 95.9% in vertebral shapes when compared to actual postop models, falling within the acceptable margins of error below 2 mm.

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Source
http://dx.doi.org/10.1016/j.media.2024.103400DOI Listing

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