Use of ultrasound imaging for assessment of the periodontium: A systematic review
Bibliographic record
Abstract
The objective of this study was to systematically review the literature regarding diagnostic applications of ultrasound imaging for evaluation of the periodontium in humans. The search was conducted on Medline, EMBASE, Web of Science, Scopus, Cochrane, and PubMed up to April 3, 2023. The studies included were exclusively human studies that assessed the periodontium with ultrasound (US) imaging (b-mode). Outcomes measured included alveolar bone level, alveolar bone thickness, gingival thickness, and blood flow quantification. References were imported to Covidence. Two reviewers conducted phases 1 and 2. The JBI risk assessment tool for cross-sectional studies was used. Extracted data included the transducer and measurements used and the study's outcomes. The search yielded 4892 studies after removing duplicates. From these, 25 studies were included and selected for extraction. Included studies retrieved outcomes from US examinations of the periodontal tissues. From the selected studies, 15 used US on natural teeth, 4 used US on implants, 2 used US on edentulous ridges, and 4 used color flow/power in US to evaluate the blood flow. The results of the present systematic review suggest that US might be a feasible and valuable diagnostic tool for the periodontium, with the potential to complement shortfalls of current radiographic technologies.
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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".