Automated Analysis of Tooth Anatomy and Pathological Conditions from Orthopantomogram using Deep Neural Networks
Bibliographic record
Abstract
This research project aims to automate the identification, labeling, and counting of teeth, as well as the classification of abnormalities and detection of caries in dental X-rays, specifically orthopantomograms (OPGs). It involves several deep neural networks and learning algorithms. The first module uses semantic segmentation with a U-net model to create masks for tooth detection, which are then refined with the YOLOv3 detector, achieving 80% accuracy. Canonical correlation analysis (CCA) helps find tooth midpoints and count the total number of teeth. The second module classifies abnormalities and pathologies using transfer learning with the Inceptionv3 model, yielding moderate accuracy. Caries detection is performed with thresholding and segmentation. The third module detects three treated pathologies—root canal treatments, crowns, and implants—using Faster RCNN and Inceptionv3, showing fair accuracy. Overall, the automated approach demonstrates promising results for enhancing X-ray image interpretation and diagnosing oral diseases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".