Predicting the Premorbid Shape of a Diseased Mandible
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Virtual surgical planning (VSP) for reconstructions of advanced mandibular neoplasms that have distorted the contour of the mandible is challenging, as the premorbid shape of the mandible is unknown. We introduce a novel modeling technique, based on a statistical shape model (SSM), that has learned the shape of a normal mandible from a set of 84 mandibles, such that given a diseased mandible, the model can determine its premorbid shape. METHODS: Eighty-four control mandibles were used to generate an SSM. Various mandibular defects were created, and the SSM was applied to predict the shape of the original mandible. The predicted and original shape of the defect were compared for accuracy using volumetric overlap and Hausdorff distance. All mandibular VSP cases in the past 2 years were reviewed to identify those that required virtual preprocessing due to significantly distorted mandibular contours. The SSM was compared to those cases requiring preprocessing and highlighted in one prospective VSP. RESULTS: The average volumetric overlap and Hausdorff distance between the defect replacement and the defect are 73.9% ± 13.3% and 4.51 mm ± 2.65 mm, respectively. The SSM is more accurate for smaller defects, and those not including the condyle. Ten out of 40 VSP cases required preprocessing using four different techniques. Qualitatively, the SSM outperformed those preprocessing techniques applied in the retrospective cases. CONCLUSIONS: The SSM can accurately predict the premorbid shape of a distorted mandible and is superior to current preprocessing techniques. The SSM was successfully applied to a retrospective series and one prospective index case. LEVEL OF EVIDENCE: 4 Laryngoscope, 131:E781-E786, 2021.
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 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 | 0.000 | 0.000 |
| 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 | 0.001 | 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".