Biomechanically driven intraoperative spine registration during navigated anterior vertebral body tethering
Bibliographic record
Abstract
The integration of pre-operative biomechanical planning with intra-operative imaging for navigated corrective spine surgery may improve surgical outcomes, as well as the accuracy and safety of manoeuvres such as pedicle screw insertion and cable tethering, as these steps are performed empirically by the surgeon. However, registration of finite element models (FEMs) of the spine remains challenging due to changes in patient positioning and imaging modalities. The purpose of this study was to develop and validate a new method registering a preoperatively constructed patient-specific FEM aimed to plan and assist anterior vertebral body tethering (AVBT) of scoliotic patients, to intraoperative cone beam computed tomography (CBCT) during navigated AVBT procedures. Prior to surgery, the 3D reconstruction of the patient's spine was obtained using biplanar radiographs, from which a patient-specific FEM was derived. The surgical plan was generated by first simulating the standing to intraoperative decubitus position change, followed by the AVBT correction techniques. Intraoperatively, a CBCT was acquired and an automatic segmentation method generated the 3D model for a series of vertebrae. Following a rigid initialization, a multi-level registration simulation using the FEM and the targeted positions of the corresponding reconstructed vertebrae from the CBCT allows for the refinement of the alignment between modalities. The method was tested with 18 scoliotic cases with a mean thoracic Cobb angle of 47° ± 7° having already undergone AVBT. The translation error of the registered FEM vertebrae to the segmented CBCT spine was 1.4 ± 1.2 mm, while the residual orientation error was 2.7° ± 2.6°, 2.8° ± 2.4° and 2.5° ± 2.8° in the coronal, sagittal, and axial planes, respectively. The average surface-to-surface distance was 1.5 ± 1.2 mm. The proposed method is a first attempt to use a patient-specific biomechanical FEM for navigated AVBT, allowing to optimize surgical strategies and screw placement during surgery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".