Classification of Salivary Gland Tumors on Ultrasound Using Artificial Intelligence: A Systematic Review and Meta-Analysis.

Journal: Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
Published Date: Jan 4, 2026

Abstract

OBJECTIVE: Accurate 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 METHODS: Two 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. RESULTS: Out 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). CONCLUSION: Artificial 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.

Authors

  • Isabelle J Chau
    Department of Otolaryngology-Head and Neck Surgery, Medical University of South Carolina, Charleston, South Carolina, USA.
  • Cory Hyun-Su Kim
    Department of Otolaryngology-Head and Neck Surgery, Medical University of South Carolina, Charleston, South Carolina, USA.
  • Shaun A Nguyen
    Department of Otolaryngology-Head and Neck Surgery, Medical University of South Carolina, 135 Rutledge Avenue, MSC 550, Charleston, SC, 29425, USA. [email protected].
  • Lauren R McCray
    Department of Otolaryngology-Head and Neck Surgery, Medical University of South Carolina, Charleston, South Carolina, USA.
  • Jesse D Murdaugh
    Department of Otolaryngology-Head and Neck Surgery, Medical University of South Carolina, Charleston, South Carolina, USA.
  • Jason G Newman
    Department of Otolaryngology-Head and Neck Surgery, Medical University of South Carolina, Charleston, South Carolina, USA.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals