Ultrasound Imaging of the Periodontium Complex: A Reliability Study
Bibliographic record
Abstract
Background: Ultrasonography is a noninvasive, low-cost diagnostic tool widely used in medicine. Recent studies have demonstrated that ultrasound imaging might have the potential to be used intraorally to assess periodontal biomarkers. Objectives: To evaluate the reliability of interlandmark distance measurements on intraoral ultrasound images of the periodontal tissues. Materials and Methods: = 31) clinics were recruited. A 20 MHz handheld intraoral ultrasound transducer was used to scan maxillary and mandibular incisors, canines, and premolars. Distances between the alveolar bone crest and cementoenamel junction (ABC-CEJ), gingival thickness (GT), and alveolar bone thickness (ABT) were measured by 3 raters. The intercorrelation coefficient (ICC) and mean absolute deviation (MAD) were calculated among and between the raters. Raters also scored images according to quality. Results: The ICC scores for intrarater reliability were 0.940 (0.932-0.947), 0.953 (0.945-0.961), and 0.859 (0.841-0.876) for ABC-CEJ, GT, and ABT, respectively. The intrarater MAD values were 0.023 (±0.019) mm, 0.014 (±0.005) mm, and 0.005 (±0.003) mm, respectively. The ICC scores for interrater reliability were 0.872 (95% CI: 0.836-0.901), 0.958 (95% CI: 0.946-0.968), and 0.836 (95% CI: 0.789-0.873) for ABC-CEJ, GT, and ABT, respectively. The interrater MAD values were 0.063 (±0.029) mm, 0.023 (±0.018) mm, and 0.027 (±0.012) mm, respectively. Conclusions: The present study showed the high reliability of ultrasound in both intrarater and interrater assessments. Results suggest there might be a potential use of intraoral ultrasound to assess periodontium.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".