Automated Cavity Detection and Classification Using Deep Learning
Bibliographic record
Abstract
Cavity detection in dental X-rays is essential for early diagnosis and treatment planning, yet traditional deep learning approaches often rely on binary classification of entire images, overlooking localized analysis. This study introduces a multi-scale AI-assisted approach for both classification and detection, leveraging the Ultralytics YOLO11 framework to compare image-level and tooth-level methodologies. The single-tooth classification model achieved a test accuracy of 0.854, while panoramic classification performed slightly better at 0.864. For cavity detection, the tooth-level model outperformed the panoramic approach, with the test set achieving mAP@50 of 0.845 compared to 0.669, with significantly higher recall (0.743 vs. 0.394), highlighting challenges in full-image localization. These findings emphasize the trade-offs between segmentation-based and direct image-based approaches, demonstrating the advantages of tooth-level analysis for improved detection accuracy. Future work will refine segmentation techniques, expand clinical datasets, and validate performance across varied imaging conditions.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".