Alveolar Bone Segmentation in Intraoral Ultrasonographs with Machine Learning
Bibliographic record
Abstract
The use of intraoral ultrasound imaging has received great attention recently due to the benefits of being a portable and low-cost imaging solution for initial and continuing care that is noninvasive and free of ionizing radiation. Alveolar bone is an important structure in the periodontal apparatus to support the tooth. Accurate assessment of alveolar bone level is essential for periodontal diagnosis. However, interpretation of alveolar bone structure in ultrasound images is a challenge for clinicians. This work is aimed at automatically segmenting alveolar bone and locating the alveolar crest via a machine learning (ML) approach for intraoral ultrasound images. Three convolutional neural network-based ML methods were trained, validated, and tested with 700, 200, and 200 images, respectively. To improve the robustness of the ML algorithms, a data augmentation approach was introduced, where 2100 additional images were synthesized through vertical and horizontal shifting as well as horizontal flipping during the training process. Quantitative evaluations of 200 images, as compared with an expert clinician, showed that the best ML approach yielded an average Dice score of 85.3%, sensitivity of 88.5%, and specificity of 99.8%, and identified the alveolar crest with a mean difference of 0.20 mm and excellent reliability (intraclass correlation coefficient ≥0.98) in less than a second. This work demonstrated the potential use of ML to assist general dentists and specialists in the visualization of alveolar bone in ultrasound images.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".