A Comprehensive Deep Learning Framework for Dental Disease Classification
Bibliographic record
Abstract
Dental diseases pose a major global health challenge, impacting billions and often leading to severe complications if undiagnosed.Limited access to dental professionals, especially in underserved regions, hampers early detection and timely treatment.This study presents a deep learning-based system for automated detection of common dental diseases, utilizing a five-layer convolutional neural network (CNN) along with Residual Networks (ResNet) and Vision Transformer (ViT) models to analyze dental images and classify them into five prevalent conditions.The model employs data augmentation to enhance generalization, confidence thresholding to identify uncertain cases, and a user-friendly interface for seamless integration into clinical workflows.Trained on a dataset split into 70% training, 15% validation, and 15% testing, the model achieved a validation accuracy of 87.6%, demonstrating its potential as a dependable diagnostic tool.Advanced image preprocessing and a scoring mechanism ensure flagged cases receive expert review, improving both reliability and safety.By streamlining diagnostics, the system facilitates early detection, reduces diagnostic inconsistencies, and expands access to dental care in resource-constrained settings.Additionally, it holds promise for dental education and research by delivering consistent and automated assessments.This work underscores the transformative impact of AI in healthcare, enhancing efficiency, accessibility, and outcomes in dental diagnostics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".