ArticleLaryngoscope investigative otolaryngology2022
Automatic classification of informative laryngoscopic images using deep learning.
Article in Laryngoscope investigative otolaryngology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 34 citations in OpenAlex.
- 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
- Real-Time AI-Based Assessment of Glottic Lesions During Flexible Laryngoscopy: Comparison with Expert Evaluation and Histopathology.Medical sciences (Basel, Switzerland) · 2026Article
- A 3-Tier AI Model for COVID-19 Triage Using Pharyngeal Images: Algorithm Development and Validation.JMIR formative research · 2026Article
- Exploring Real-Time Tracking of Vocal Fold Polyps in Video-Stroboscopy Using Deep Learning.The Laryngoscope · 2026Article
- Feasibility of Real-Time Automated Vocal Fold Motion Tracking for In-Office Laryngoscopy.The Laryngoscope · 2026Article
- Vocal Fold Disorders Classification and Optimization of a Custom Video Laryngoscopy Dataset Through Structural Similarity Index and a Deep Learning-Based Approach.Journal of clinical medicine · 2025Article
- A Deep-Learning Model for Multi-class Audio Classification of Vocal Fold Pathologies in Office Stroboscopy.The Laryngoscope · 2025Article
- Binary Classification of Laryngeal Images Utilising ResNet-50 CNN Architecture.Indian journal of otolaryngology and head and neck surgery : official publication of the Association of Otolaryngologists of India · 2025Article
- Improving Laryngoscopy Image Analysis Through Integration of Global Information and Local Features in VoFoCD Dataset.Journal of imaging informatics in medicine · 2024Article
- An automated approach for real-time informative frames classification in laryngeal endoscopy using deep learning.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 · 2024Article
- A deep learning pipeline for automated classification of vocal fold polyps in flexible 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 · 2024Article
- Development and Application of Automated Vocal Fold Tracking Software in a Rat Surgical Model.The Laryngoscope · 2024Article
- Deep Learning Techniques and Imaging in Otorhinolaryngology-A State-of-the-Art Review.Journal of clinical medicine · 2023Review
- A Novel Framework of Manifold Learning Cascade-Clustering for the Informative Frame Selection.Diagnostics (Basel, Switzerland) · 2023Article
- Automatic classification of informative laryngoscopic images using deep learning.Laryngoscope investigative otolaryngology · 2022Article
Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: This study aims to develop and validate a convolutional neural network (CNN)-based algorithm for automatic selection of informative frames in flexible laryngoscopic videos. The classifier has the potential to aid in the development of computer-aided diagnosis systems and reduce data processing time for clinician-computer scientist teams. Methods: A dataset of 22,132 laryngoscopic frames was extracted from 137 flexible laryngostroboscopic videos from 115 patients. 55 videos were from healthy patients with no laryngeal pathology and 82 videos were from patients with vocal fold polyps. The extracted frames were manually labeled as informative or uninformative by two independent reviewers based on vocal fold visibility, lighting, focus, and camera distance, resulting in 18,114 informative frames and 4018 uninformative frames. The dataset was split into training and test sets. A pre-trained ResNet-18 model was trained using transfer learning to classify frames as informative or uninformative. Hyperparameters were set using cross-validation. The primary outcome was precision for the informative class and secondary outcomes were precision, recall, and F1-score for all classes. The processing rate for frames between the model and a human annotator were compared. Results: The automated classifier achieved an informative frame precision, recall, and F1-score of 94.4%, 90.2%, and 92.3%, respectively, when evaluated on a hold-out test set of 4438 frames. The model processed frames 16 times faster than a human annotator. Conclusion: The CNN-based classifier demonstrates high precision for classifying informative frames in flexible laryngostroboscopic videos. This model has the potential to aid researchers with dataset creation for computer-aided diagnosis systems by automatically extracting relevant frames from laryngoscopic videos.
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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.