Automatic Assessment of Periodontium Complex in Intraoral Ultrasound Videos
Bibliographic record
Abstract
Intraoral ultrasound (IUS) is emerging as a valuable imaging modality in dentistry, offering noninvasive, radiation-free, real-time visualization of periodontal structures. Unlike traditional imaging methods, IUS enables dynamic assessments during clinical procedures, supporting diagnostic and treatment-planning capabilities. The accurate evaluation of parameters such as alveolar bone level (ABL), gingival thickness (GT), and alveolar bone thickness (ABT) is critical for diagnosing periodontal diseases. However, current assessment techniques are typically manual, time-consuming, and based on static images, leading to inter-operator variability and limiting real-time application. To address these gaps, this study aimed to develop OralSAM, an end-to-end machine learning network for automated segmentation and quantitative assessment of periodontal structures in IUS videos. The network segments gingiva, enamel, alveolar bone, and cementum, followed by a morphological analysis pipeline to extract clinically relevant measurements. A total of 158 IUS videos from 30 orthodontic patients were included, and the dataset was split into training, validation, and testing subsets following a 6:2:2 ratio. The segmentation performance of OralSAM, evaluated against expert-annotated ground truth, demonstrated high segmentation accuracy across key periodontal structures. Morphological measurements derived from the machine learning network also exhibited strong inter-rater reliability, as confirmed by Bland-Altman analysis, which demonstrated narrow limits of agreement (LOAs) for ABL (mean bias = -0.063 mm, LOA = -0.771 to 0.646 mm), GT (mean bias = -0.063 mm, LOA = -0.24 to 0.115 mm), and ABT (mean bias = -0.002 mm, LOA = -0.104 to 0.1 mm). The intraclass correlation coefficients were 0.893 (95% confidence interval [CI], 0.864 to 0.915) for ABL, 0.918 (95% CI, 0.768 to 0.960) for GT, and 0.848 (95% CI, 0.806 to 0.880) for ABT. These findings highlight OralSAM's capability to accurately delineate periodontal structures and provide consistent assessments. The proposed framework shows strong potential for integration into routine chairside workflows, enabling early detection, real-time monitoring, and personalized management of periodontal disease.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".