Improving Cranial Vault Remodeling for Unilateral Coronal Craniosynostosis—Introducing Automated Surgical Planning
Bibliographic record
Abstract
Study Design: Cranial vault remodeling (CVR) for unicoronal synostosis is challenging due to the asymmetric nature of the deformity. Computer-automated surgical planning has demonstrated success in reducing the subjectivity of decision making in CVR in symmetric subtypes. This proof of concept study presents a novel method using Boolean functions and image registration to automatically suggest surgical steps in asymmetric craniosynostosis. Objective: The objective of this study is to introduce automated surgical planning into a CVR virtual workflow for an asymmetric craniosynostosis subtype. Methods: Virtual workflows were developed using Geomagic Freeform Plus software. Hausdorff distances and color maps were used to compare reconstruction models to the preoperative model and a control skull. Reconstruction models were rated as high or low performing based on similarity to the normal skull and the amount of advancement of the frontal bone (FB) and supra-orbital bar (SOB). Fifteen partially and fully automated workflow iterations were carried out. Results: FB and SOB advancement ranged from 3.08 to 10.48 mm, and -1.75 to 7.78 mm, respectively. Regarding distance from a normal skull, models ranged from .85 to 5.49 mm at the FB and 5.40 to 10.84 mm at the SOB. An advancement of 8.43 mm at the FB and 7.73 mm at the SOB was achieved in the highest performing model, and it differed to a comparative normal skull by .02 mm at the FB and .48 mm at the SOB. Conclusions: This is the first known attempt at developing an automated virtual surgical workflow for CVR in asymmetric craniosynostosis. Key regions of interest were outlined using Boolean operations, and surgical steps were suggested using image registration. These techniques improved post-operative skull morphology.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".