ArticleEuropean 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 Surgery2024
An automated approach for real-time informative frames classification in laryngeal endoscopy using deep learning.
Article in 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, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 3 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Current role of artificial intelligence and machine learning: is their application feasible in pediatric upper airway obstructive disorders?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 · 2026Pooled it
- A Systematic Review of the Clinical Impact of Implementing Artificial Intelligence in Upper Aerodigestive Tract Endoscopy.Head & neck · 2025Pooled it
- Artificial Intelligence in Laryngeal Cancer Detection: A Systematic Review and Meta-Analysis.Current oncology (Toronto, Ont.) · 2025Pooled it
- LUCID: Intelligent Informative Frame Selection in Otoscopy for Enhanced Diagnostic Utility.Diagnostics (Basel, Switzerland) · 2026Article
- An interpretable multi-instance learning method for accurate differentiation of malignant and benign laryngeal lesions in laryngoscopy.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 · 2026Article
- Real-Time AI-Based Assessment of Glottic Lesions During Flexible Laryngoscopy: Comparison with Expert Evaluation and Histopathology.Medical sciences (Basel, Switzerland) · 2026Article
- Deep Learning-Based Objective Quantification of Nasopharyngeal Endoscopic Findings for Standardized Assessment of Inflammation.Diagnostics (Basel, Switzerland) · 2026Article
- Contemporary Indications and Technical Limits of Transoral Laser Microsurgery for Laryngeal Squamous Cell Carcinoma.Advances in therapy · 2026Review
- A Multimodal Approach for Deep-Learning Classification of Vocal Fold Pathologies in Stroboscopy.The Laryngoscope · 2026Article
- CKAD-Net: class-guided key-feature aggregation and adaptive decision network for vocal cord lesion prediction.Scientific reports · 2026Article
- Multicenter Clinical Validation of an Artificial Intelligence Diagnostic Classification Model for Laryngoscopy Images.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2026Article
- Development and validation of a deep learning model for identifying high-quality laryngoscopic images.Scientific reports · 2026Article
- Deciphering the Significance of Vocal Fold Motility in Laryngeal Cancer: Time for a Paradigm Shift?The Laryngoscope · 2025Article
- Feasibility of improving vocal fold pathology image classification with synthetic images generated by DDPM-based GenAI: a pilot study.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 · 2025Article
- Democratizing cancer detection: artificial intelligence-enhanced endoscopy could address global disparities in head and neck cancer outcomes.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 · 2025Review
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeInformative image selection in laryngoscopy has the potential for improving automatic data extraction alone, for selective data storage and a faster review process, or in combination with other artificial intelligence (AI) detection or diagnosis models. This paper aims to demonstrate the feasibility of AI in providing automatic informative laryngoscopy frame selection also capable of working in real-time providing visual feedback to guide the otolaryngologist during the examination.
methodsSeveral deep learning models were trained and tested on an internal dataset (n = 5147 images) and then tested on an external test set (n = 646 images) composed of both white light and narrow band images. Four videos were used to assess the real-time performance of the best-performing model.
resultsResNet-50, pre-trained with the pretext strategy, reached a precision = 95% vs. 97%, recall = 97% vs, 89%, and the F1-score = 96% vs. 93% on the internal and external test set respectively (p = 0.062). The four testing videos are provided in the supplemental materials.
conclusionThe deep learning model demonstrated excellent performance in identifying diagnostically relevant frames within laryngoscopic videos. With its solid accuracy and real-time capabilities, the system is promising for its development in a clinical setting, either autonomously for objective quality control or in conjunction with other algorithms within a comprehensive AI toolset aimed at enhancing tumor detection and diagnosis.
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