Diagnostic accuracy of deep learning-based algorithms in laryngoscopy: a systematic review and meta-analysis

Purpose Laryngoscopy is routinely used for suspicious vocal cord lesions with limited performance. Accumulated studies have demonstrated the bright prospect of deep learning in processing medical imaging. In this study, we perform a systematic review and meta-analysis to investigate diagnostic utili...

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Published in:European archives of oto-rhino-laryngology Vol. 282; no. 1; pp. 351 - 360
Main Authors: Du, Shengyi, Guo, Jin, Huang, Donghai, Liu, Yong, Zhang, Xin, Lu, Shanhong
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2025
Springer Nature B.V
Subjects:
Algorithms
Deep Learning
Head and Neck Surgery
Humans
Laryngeal Neoplasms - diagnosis
Laryngeal Neoplasms - diagnostic imaging
Laryngology
Laryngoscopy - methods
Medicine
Medicine & Public Health
Neurosurgery
Otorhinolaryngology
Sensitivity and Specificity
Deep learning
Accuracy
Diagnosis
Laryngoscopy
ISSN:0937-4477, 1434-4726, 1434-4726
Online Access:Get full text
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Summary:Purpose Laryngoscopy is routinely used for suspicious vocal cord lesions with limited performance. Accumulated studies have demonstrated the bright prospect of deep learning in processing medical imaging. In this study, we perform a systematic review and meta-analysis to investigate diagnostic utility of deep learning in laryngoscopy. Methods The study was performed according to the Primary Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. We comprehensively retrieved articles from the PubMed, Scopus, Embase, and Web of Science up to July 14, 2024. Eligible studies with application of deep learning algorithm in laryngoscopy were assessed and enrolled by two independent investigators. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and diagnostic odds ratio with 95% confidence intervals (CIs) were calculated using a random effects model. Results We retained 9 eligible studies adding up to 106,175 endoscopic images for the meta-analysis. The pooled sensitivity and specificity to diagnose laryngeal cancer were 0.95(95% CI: 0.85–0.98) and 0.96 (95% CI: 0.91–0.98). The area under the curve of deep learning was 0.99 (95%CI: 0.97–0.99). Conclusion Deep learning demonstrated excellent diagnostic efficacy in assessing laryngeal cancer with laryngoscope images in current studies, which manifests its potential of aiding endoscopist for laryngeal cancer diagnosis and clinical decision making. Highlight This study performed the first pool analysis of deep learning algorithms in aiding laryngoscopy. This study retrieved and screened of 1825 candidate studies following the guidelines of meta-analysis. This study revealed the excellent diagnostic efficacy of the deep learning in assessing laryngeal cancer.
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ISSN:0937-4477
1434-4726
1434-4726
DOI:10.1007/s00405-024-09049-2