Investigating the Role of Ultrasound in the Diagnosis of Oral Lesions: A Scoping Review
Bibliographic record
Abstract
AIM: To provide an overview of the diagnostic potential of Ultrasound to assess oral lesions using Magnetic Resonance Imaging, Computed Tomography/Cone-beam computed tomography, or histopathology as the reference standard. METHODS: A literature search was conducted from the following databases: OVID Medline and Embase, Web of Science, Pubmed, and Scopus. The Joanna Briggs Institute Checklist for Systematic Reviews and Research Syntheses tool was used to assess the risk of bias. RESULTS: Thirty-four studies were included. One study assessed soft tissue lesions, one study assessed intraosseous lesions, and 32 studies assessed malignancies. Ultrasound demonstrated its ability to recognize biomarkers of a diverse range of soft tissue lesions with sensitivity and specificity over 90%. Sensitivity and specificity over 90% were also found for the detection of ameloblastoma proliferation. From studies investigating malignancies, 32% measured the depth of invasion, and 56% measured tumor thickness. The main method of analysis was correlation in 68% of the studies, followed by 25% of the studies assessing sensitivity and specificity. CONCLUSION: Ultrasound has the potential to provide accurate information on the characteristics of benign oral lesions and malignancies. It can be used as an initial method of assessment or screening to aid in diagnosis and treatment planning.
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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 | 0.019 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.032 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".