ReviewJournal of clinical medicine2022
Artificial Intelligence in Laryngeal Endoscopy: Systematic Review and Meta-Analysis.
Review in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews.Journal of medical Internet research · 2025Pooled it
- Application of artificial intelligence in laryngeal lesions: a systematic review and meta-analysis.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025Pooled it
- Role of artificial intelligence in head and neck surgery - A narrative review.Turkish journal of surgery · 2026Article
- Laryngeal Anatomy and Morphometry: Foundations for Interdisciplinary Collaboration and Personalized Management of Laryngeal Pathology.Diagnostics (Basel, Switzerland) · 2026Review
- Real-Time AI-Based Assessment of Glottic Lesions During Flexible Laryngoscopy: Comparison with Expert Evaluation and Histopathology.Medical sciences (Basel, Switzerland) · 2026Article
- Videomics and artificial intelligence in endoscopic diagnosis of laryngeal lesions: mapping current evidence through a scoping review.Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale · 2026Review
- Feasibility of Real-Time Automated Vocal Fold Motion Tracking for In-Office Laryngoscopy.The Laryngoscope · 2026Article
- Future Use of AI in Diagnostic Medicine: 2-Wave Cross-Sectional Survey Study.Journal of medical Internet research · 2025Article
- Comparison of clinical nasal endoscopy, optical biopsy, and artificial intelligence in early diagnosis and treatment planning in laryngeal cancer: a prospective observational study.Frontiers in oncology · 2025Article
- Summary of the best evidence for vocal rehabilitation training in adults with unilateral vocal fold paralysis.Annals of medicine and surgery (2012) · 2024Review
- Review
- Diagnostic Accuracy of Artificial Intelligence in Endoscopy: Umbrella Review.JMIR medical informatics · 2024Review
- Clinical significance of type IV vascularization of laryngeal lesions according to the Ni classification.Frontiers in oncology · 2024Article
- Computer-Aided Diagnosis of Laryngeal Cancer Based on Deep Learning with Laryngoscopic Images.Diagnostics (Basel, Switzerland) · 2023Article
- Interpretable Computer Vision to Detect and Classify Structural Laryngeal Lesions in Digital Flexible Laryngoscopic Images.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2023Article
- Contact Endoscopy - Narrow Band Imaging (CE-NBI) data set for laryngeal lesion assessment.Scientific data · 2023Article
- Review
- Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions.NPJ digital medicine · 2022Review
- Narrative Review of Classification Systems Describing Laryngeal Vascularity Using Advanced Endoscopic Imaging.Journal of clinical medicine · 2022Review
- An Improvised Deep-Learning-Based Mask R-CNN Model for Laryngeal Cancer Detection Using CT Images.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEarly 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.
methodsFive 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.
resultsAll 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 (
conclusionIn assessing images of laryngeal lesions, AI demonstrates extraordinarily high accuracy, sensitivity, and specificity.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.