SynthesisOtolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery2026
Classification of Salivary Gland Tumors on Ultrasound Using Artificial Intelligence: A Systematic Review and Meta-Analysis.
Synthesis in Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveAccurate classification of salivary gland tumors is critical to guiding appropriate management. This study evaluates the diagnostic performance of artificial intelligence models in classifying salivary gland tumors on ultrasound. DATA SOURCES: A comprehensive search of CINAHL, PubMed, and Scopus was conducted through January 28, 2025. REVIEW
methodsTwo independent reviewers screened articles and extracted data following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Studies evaluating the diagnostic performance of artificial intelligence in classifying salivary gland tumors were included for fixed and random-effects meta-analyses. The Quality Assessment of Diagnostic Accuracy Studies-2 tool for systematic reviews of diagnostic accuracy studies was used to assess study quality and risk of bias.
resultsOut of 741 articles identified, 12 studies (N = 4721) met inclusion criteria. Nine studies evaluated artificial intelligence models differentiating benign from malignant tumors, and three studies assessed classification of pleomorphic adenomas versus Warthin tumors. For benign versus malignant tumors, sensitivity was 0.91 (95% CI: 0.86, 0.95), specificity was 0.86 (95% CI: 0.80, 0.92), and accuracy was 0.85 (95% CI: 0.81, 0.90). For pleomorphic adenomas versus Warthin tumors, sensitivity was 0.81 (95% CI: 0.74, 0.89), specificity was 0.88 (95% CI: 0.81, 0.95), and accuracy was 0.84 (95% CI: 0.79, 0.90).
conclusionArtificial intelligence models demonstrate strong diagnostic accuracy in ultrasound-based classification of salivary gland tumors. These results highlight the potential of artificial intelligence as a diagnostic tool, though broader validation is needed before routine clinical implementation.
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