AI-Driven Dental Radiography Analysis: Enhancing Diagnosis and Education Through YOLOv8 and Eigen-CAM
Bibliographic record
Abstract
This study is an artificial intelligence (AI)-supported system that aims to help dentists and students by analyzing dental X-rays and detecting certain diseases in teeth.This system aims to help students in the learning process by quickly detecting procedures such as dentin decay, root canal treatment, implants, crowns, fillings in dental X-rays.The datasets obtained through Roboflow were subjected to labeling process.The dataset consists of approximately 2500 dental X-ray images containing dental diseases and procedures performed on teeth, consisting of 5 different classes.The classes identified in these images were labeled.After this labeling process, a deep learning model was developed using YOLOv8 architecture.Eigen-CAM was added to the model and its performance was tested.Eigen-CAM helped to finalize the results of the model by visualizing them.After all these processes, the model was integrated into a web interface and made available for use.The results of this study show that the proposed method is very fast and effective in analyzing dental X-rays.The results of the study have made significant contributions to dentists and dental students in terms of early diagnosis and learning process and have the potential to positively affect clinical decision support processes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".