P.120 Rapid intraoperative reconstruction of cranial implants for craniotomy procedures: a feasibility study
Bibliographic record
Abstract
Background: The aim of this study was to investigate intraoperative methods to generate patient-specific PMMA bone implants during a craniotomy. The proposed methods combine a cost-efficient, and non-invasive structured light scanner (SLS) as an imaging modality and a prototype printer for rapid generation of implant molds. Methods: This simulation study was performed using retrospective data from three craniotomy patients. The extracted bone flap and the cranial defect were scanned using a SLS, which generates a 3D surface model of an object by projecting a series of light-patterns on it. Prototype printed implant models were generated using two different techniques. The molds were then used to shape PMMA bone implants. These implants were evaluated regarding their accuracy to reconstruct the natural skull anatomy and compared to freehand formed implants. Results: The patient-specific bone implants reconstructed the preoperative anatomy with an average RMS error of 1.37mm (StDev 0.27), compared to an error of 1.5mm (StDev 0.43) for the freehand shaped implants. On average the intraoperative scanning time was 4.7min. The average time to generate and print the implant molds was 204 min. Conclusions: Results of this study have shown great promise for the proposed method to be used for patient-specific bone flap reconstruction during craniotomies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 |
| 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".