Prediction of the upright articulated spine shape in the operating room using conditioned neural kernel fields
Bibliographic record
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 . 5 mm 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. • We introduce a forecasting method of the standing spine shape during surgery. • An articulated neural kernel field disentangles spine’s latent representations. • An articulation/shape network is used to generate the standing shape. • Multi-view model based on view-divergent Transformers infers intra-op 3D model. • Clinical efficacy is demonstrated in spine surgery plans with several deformations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".