Registration of Ultrasound and CBCT Images for Enhancing Tooth-Periodontinum Visualization: a Feasibility Study
Bibliographic record
Abstract
Malocclusion, a common dental anomaly, can lead to oral function issues such as difficulty in jaw movement, chewing, speech, and high susceptibility to periodontal diseases. High resolution cone-beam computed tomography (CBCT) images provide sharp visualization of the alveolar bone while delineation of gingiva is inferior. Ultrasound (US) images can delineate the thickness and extension of gingiva while partially showing the thickness of alveolar bones. Accurate registration of CBCT and US images of the tooth-periodontium would allow oral clinicians to visualize the gingiva and alveolar bone for proper diagnosis and orthodontic treatment. A probability-based point set registration using coherent point drift algorithm is proposed to tackle the problem of aligning CBCT and US images. The proposed algorithm was evaluated with images from human volunteers. The results indicate that the method is reasonably robust, and yield mean errors below the clinically accepted tolerance level of 0.5 mm. The limits of agreement between CBCT and US images measured in terms of landmark-based evaluations were [-0.22 mm, 0.26 mm] for intra-rater and [-0.19 mm, 0.21 mm] for inter-rater, respectively. More data are currently acquired to validate the accuracy and efficacy of the method.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".