Diagnostic Accuracy of Deep Learning-based Algorithms in Laryngoscopy: a Systematic Review and Meta-analysis

Overview

Journal Eur Arch Otorhinolaryngol

Specialty Otorhinolaryngology

Date 2024 Oct 24

PMID 39446141

Authors

Shengyi Du

Jin Guo

Donghai Huang

Yong Liu

Xin Zhang

Shanhong Lu

Affiliations

Soon will be listed here.

Abstract

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.

Citing Articles

Authors' response to the letter to the editor: "Conventional versus diode laser stapedotomy: audiological outcomes and clinical safety".

Ordonez Ordonez L, Perdomo D, Saboya C, Mejia F, Medina-Parra J, Martinez E Eur Arch Otorhinolaryngol. 2024; 281(11):6043-6046.

PMID: 39242422 DOI: 10.1007/s00405-024-08946-w.

References

Johnson D, Burtness B, Leemans C, Lui V, Bauman J, Grandis J . Head and neck squamous cell carcinoma. Nat Rev Dis Primers. 2020; 6(1):92. PMC: 7944998. DOI: 10.1038/s41572-020-00224-3. View

Lin C, Cheng W, Liu X, Li H, Song Y . The global, regional, national burden of laryngeal cancer and its attributable risk factors (1990-2019) and predictions to 2035. Eur J Cancer Care (Engl). 2022; 31(6):e13689. DOI: 10.1111/ecc.13689. View

Ye E, Huang J, Wang J, Zhao Y, Niu D, Liu J . Trend and projection of larynx cancer incidence and mortality in China from 1990 to 2044: A Bayesian age-period-cohort modeling study. Cancer Med. 2023; 12(15):16517-16530. PMC: 10469639. DOI: 10.1002/cam4.6239. View

Alonso-Coello P, Rigau D, Sanabria A, Plaza V, Miravitlles M, Martinez L . Quality and strength: the GRADE system for formulating recommendations in clinical practice guidelines. Arch Bronconeumol. 2013; 49(6):261-7. DOI: 10.1016/j.arbres.2012.12.001. View

McInnes M, Moher D, Thombs B, McGrath T, Bossuyt P, Clifford T . Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies: The PRISMA-DTA Statement. JAMA. 2018; 319(4):388-396. DOI: 10.1001/jama.2017.19163. View

Ren J, Jing X, Wang J, Ren X, Xu Y, Yang Q . Automatic Recognition of Laryngoscopic Images Using a Deep-Learning Technique. Laryngoscope. 2020; 130(11):E686-E693. DOI: 10.1002/lary.28539. View

Zhao Q, He Y, Wu Y, Huang D, Wang Y, Sun C . Vocal cord lesions classification based on deep convolutional neural network and transfer learning. Med Phys. 2021; 49(1):432-442. DOI: 10.1002/mp.15371. View

Xiong H, Lin P, Yu J, Ye J, Xiao L, Tao Y . Computer-aided diagnosis of laryngeal cancer via deep learning based on laryngoscopic images. EBioMedicine. 2019; 48:92-99. PMC: 6838439. DOI: 10.1016/j.ebiom.2019.08.075. View

Yan P, Li S, Zhou Z, Liu Q, Wu J, Ren Q . Automated detection of glottic laryngeal carcinoma in laryngoscopic images from a multicentre database using a convolutional neural network. Clin Otolaryngol. 2023; 48(3):436-441. DOI: 10.1111/coa.14029. View

10.

Stanikova L, Kantor P, Fedorova K, Zelenik K, Kominek P . Clinical significance of type IV vascularization of laryngeal lesions according to the Ni classification. Front Oncol. 2024; 14:1222827. PMC: 10851150. DOI: 10.3389/fonc.2024.1222827. View

11.

Sampieri C, Baldini C, Azam M, Moccia S, Mattos L, Vilaseca I . Artificial Intelligence for Upper Aerodigestive Tract Endoscopy and Laryngoscopy: A Guide for Physicians and State-of-the-Art Review. Otolaryngol Head Neck Surg. 2023; 169(4):811-829. DOI: 10.1002/ohn.343. View

12.

Yao P, Usman M, Chen Y, German A, Andreadis K, Mages K . Applications of Artificial Intelligence to Office Laryngoscopy: A Scoping Review. Laryngoscope. 2021; 132(10):1993-2016. DOI: 10.1002/lary.29886. View

13.

LeCun Y, Bengio Y, Hinton G . Deep learning. Nature. 2015; 521(7553):436-44. DOI: 10.1038/nature14539. View

14.

Litjens G, Kooi T, Bejnordi B, Setio A, Ciompi F, Ghafoorian M . A survey on deep learning in medical image analysis. Med Image Anal. 2017; 42:60-88. DOI: 10.1016/j.media.2017.07.005. View

15.

Albano D, Galiano V, Basile M, Di Luca F, Gitto S, Messina C . Artificial intelligence for radiographic imaging detection of caries lesions: a systematic review. BMC Oral Health. 2024; 24(1):274. PMC: 10894487. DOI: 10.1186/s12903-024-04046-7. View

16.

Su Y, Li D, Chen X . Lung Nodule Detection based on Faster R-CNN Framework. Comput Methods Programs Biomed. 2020; 200:105866. DOI: 10.1016/j.cmpb.2020.105866. View