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Review
. 2022 May 12;11(10):2752.
doi: 10.3390/jcm11102752.

Artificial Intelligence in Laryngeal Endoscopy: Systematic Review and Meta-Analysis

Michał Żurek  1   2 , Kamil Jasak  3 , Kazimierz Niemczyk  1 , Anna Rzepakowska  1
Affiliations
Review

Artificial Intelligence in Laryngeal Endoscopy: Systematic Review and Meta-Analysis

Michał Żurek et al. J Clin Med. .

Abstract

Background: Early diagnosis of laryngeal lesions is necessary to begin treatment of patients as soon as possible to preserve optimal organ functions. Imaging examinations are often aided by artificial intelligence (AI) to improve quality and facilitate appropriate diagnosis. The aim of this study is to investigate diagnostic utility of AI in laryngeal endoscopy.

Methods: Five databases were searched for studies implementing artificial intelligence (AI) enhanced models assessing images of laryngeal lesions taken during laryngeal endoscopy. Outcomes were analyzed in terms of accuracy, sensitivity, and specificity.

Results: All 11 studies included presented an overall low risk of bias. The overall accuracy of AI models was very high (from 0.806 to 0.997). The accuracy was significantly higher in studies using a larger database. The pooled sensitivity and specificity for identification of healthy laryngeal tissue were 0.91 and 0.97, respectively. The same values for differentiation between benign and malignant lesions were 0.91 and 0.94, respectively. The comparison of the effectiveness of AI models assessing narrow band imaging and white light endoscopy images revealed no statistically significant differences (p = 0.409 and 0.914).

Conclusion: In assessing images of laryngeal lesions, AI demonstrates extraordinarily high accuracy, sensitivity, and specificity.

Keywords: accuracy; artificial intelligence; laryngoscopy; larynx; lesion; sensitivity; specificity.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Flow diagram of the systematic review search.
Figure 2
Figure 2
QUADAS-2 assessment of bias and applicability.
Figure 3
Figure 3
Dot plot of the accuracy of included studies (there are more dots than studies because some research analyzed more than one classification of laryngeal lesions). The dark blue points represent the group of studies for which the linear regression equation was calculated. The remaining studies are marked with light blue points.
Figure 4
Figure 4
Forest plot and ROC curve illustrating the diagnostic performance of AI identifying healthy laryngeal tissue.
Figure 5
Figure 5
Forest plot and ROC curve illustrating the diagnostic performance of AI distinguishing benign and malignant laryngeal lesions.
Figure 6
Figure 6
Forest plot illustrating the differences in diagnostic performance of AI using WLE and NBI.
See this image and copyright information in PMC

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