Comparison of ultrasound imaging and cone-beam computed tomography for examination of the alveolar bone level: A systematic review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: The current methods to image alveolar bone in humans include intraoral 2D radiography and cone-beam computed tomography (CBCT). However, these methods expose the subject to ionizing radiation. Therefore, ultrasound imaging has been investigated as an alternative technique, as it is both non-invasive and free from ionizing radiation. In order to assess the validity and reliability of ultrasonography in visualizing alveolar bone, a systematic review was conducted comparing ultrasound imaging to CBCT for examination of the alveolar bone level. STUDY DESIGN: Seven databases were searched. Studies addressing examination of alveolar bone level via CBCT and ultrasound were selected. Risk of bias under Cochrane guidelines was used as a methodological quality assessment tool. RESULTS: All the four included studies were ex vivo studies that used porcine or human cadaver samples. The alveolar bone level was measured by the distance from the alveolar bone crest to certain landmarks such as cemento-enamel junction or gingival margin. The risk of bias was found as low. The mean difference between ultrasound and CBCT measurements ranged from 0.07 mm to 0.68 mm, equivalent to 1.6% - 8.8%. CONCLUSIONS: There is currently preliminary evidence to support the use of ultrasonography as compared to CBCT for the examination of alveolar bone level. Further studies comparing ultrasound to gold standard methods would be necessary to help validate the accuracy of ultrasonography as a diagnostic technique in periodontal imaging.
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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 | 0.007 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| 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".