Artificial intelligence-assisted identification of condensing osteitis and idiopathic osteosclerosis on panoramic radiographs
Bibliographic record
Abstract
Idiopathic osteosclerosis (IOS) and condensing osteitis (CO) represent radiopaque lesions often detected incidentally within the jaws, posing substantial diagnostic challenges due to their overlapping radiographic characteristics. The objective of this study was to assess the diagnostic efficacy of YOLOv8 and YOLOv11 deep learning algorithms in the identification of IOS and CO lesions on panoramic radiographs. A comprehensive collection of 1,000 panoramic images was retrospectively gathered and meticulously annotated utilizing a bounding box approach by two proficient oral and maxillofacial radiologists. All images were standardized to a resolution of 640 × 640 pixels and segregated into training (70%), validation (15%), and testing (15%) subsets. The performance of the models was evaluated based on metrics including accuracy, sensitivity, precision, F1 score, and the area under the receiver operating characteristic curve (AUC). YOLOv11 achieved notable precision scores of 98.8% for IOS and 97.1% for CO, alongside F1 scores of 96.8% and 95.6%, respectively. Conversely, YOLOv8 produced precision scores of 96.6% for IOS and 91.4% for CO, with F1 scores of 94% and 90%. These findings illustrate that AI-enhanced deep learning models possess the capability to accurately identify IOS and CO lesions, thereby presenting opportunities to improve diagnostic consistency, avert unnecessary invasive procedures, and facilitate more effective treatment planning within clinical practice.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".