Application of Artificial Intelligence in Laryngeal Lesions: a Systematic Review and Meta-analysis

Overview

Journal Eur Arch Otorhinolaryngol

Specialty Otorhinolaryngology

Date 2024 Nov 22

PMID 39576322

Authors

Alejandro R Marrero-Gonzalez

Tanner J Diemer

Shaun A Nguyen

Terence J M Camilon

Kirsten Meenan

Ashli ORourke

Affiliations

Soon will be listed here.

Abstract

Objective: The objective of this systematic review and meta-analysis was to evaluate the diagnostic accuracy of AI-assisted technologies, including endoscopy, voice analysis, and histopathology, for detecting and classifying laryngeal lesions.

Methods: A systematic search was conducted in PubMed, Embase, etc. for studies utilizing voice analysis, histopathology for laryngeal lesions, or AI-assisted endoscopy. The results of diagnostic accuracy, sensitivity and specificity were synthesized by a meta-analysis.

Results: 12 studies employing AI-assisted endoscopy, 2 studies for voice analysis, and 4 studies for histopathology were included in the meta-analysis. The combined sensitivity of AI-assisted endoscopy was 91% (95% CI 87-94%) for the classification of benign from malignant lesions and 91% (95% CI 90-93%) for lesion detection. The highest accuracy pooled in detecting lesions versus healthy tissue was the AI-aided endoscopy was 94% (95% CI 92-97%).

Conclusions: For laryngeal lesions, AI-assisted endoscopy shows excellent diagnosis accuracy. But more sizable prospective trials are needed to confirm the practical clinical value.

Citing Articles

Clinical and Pathological Staging Discrepancies in Laryngeal Cancer: A Systematic Review.

Pecorari G, Lorenzi A, Caria M, Motatto G, Riva G Cancers (Basel). 2025; 17(3).

PMID: 39941822 PMC: 11815896. DOI: 10.3390/cancers17030455.

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