Fully-automated Alveolar Bone Level Measurements in Adolescents via Landmark Localization in Intraoral Ultrasound Videos
Bibliographic record
Abstract
Accurate and automatic assessment of alveolar bone level in ultrasound videos is crucial for orthodontic treatment and diagnosis, as manual interpretation is time-consuming and clinicians exhibit substantial interobserver variation. A systematic approach for quantifying alveolar bone loss involves the direct measurements of the alveolar bone level (ABL), the distance between the cementoenamel junction and alveolar bone crest. In this paper, we propose an end-to-end landmark localizing network by combining a convolutional neural network and Swin-transformer architecture to effectively localize the cementoenamel junction and alveolar bone crest in intraoral ultrasound videos and automatically measure the ABL. In addition, key frames and non-key frames are identified and discarded based on an uncertainty prior. This study used ultrasound videos of 147 teeth acquired from 20 orthodontic adolescent patients. Experimental studies have been performed and compared with state-of-the-art models to prove the feasibility of the proposed architecture.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".