Exploring the Use of Generative Adversarial Networks for Automated Dental Preparation Design
Bibliographic record
Abstract
Dental decay is an important disease that requires immediate treatment. Without proper medical care, it progressively affects the rest of the body. To remove a dental decay, dentists must prepare the tooth. The process of shaping extracoronal preparations demonstrates significant variability between dental practitioners. This task is time-intensive and consumes resources, often increasing the number of patient sessions. Current deep learning models have shown remarkable results on 3D shape generation. However, their use in digital dentistry has been limited to crown generation. In this study, we propose a dental prior deformation model (DPD), combining both the unsupervised generation of dental preparations from a tooth prior and point-to-mesh reconstruction. Specifically, DPD allows dentists to generate a variety of dental preparations from a dental prior to assessing the preparation choices automatically before the operation. Experiments demonstrate that a personalized global prior-with a maximum Hausdorff distance of 6.40 mm and a maximum Chamfer distance of 6.36 mm from the tooth prior across two resolutions-produces visually accurate results suitable for margin line detection. The variety of choices from DPD helps to generate and visualize position-specific preparations before clinical operations. Our code for these experiments is available at https://github.com/ImaneChafi/DPD.git
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".