Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Cervical anatomical variants: Embryological basis and surgical relevance in neck dissection - a narrative review.

Journal of stomatology, oral and maxillofacial surgery·2026
Same author

Ultra-high frequency ultrasound of labial salivary glands in Sjögren's disease: diagnostic accuracy and patient stratification.

RMD open·2026
Same author

From Conventional Septoplasty to Patient-Specific Cartilage Reshaping: A Systematic Review of Laser-Mediated and Electromechanical Approaches.

Journal of personalized medicine·2026
Same author

Personalized Selection of Inferior Turbinate Surgery Based on Structural Phenotyping: A Structured Narrative Review and Proposed Decision-Making Framework.

Journal of personalized medicine·2026
Same author

Elective neck dissection in clinically early stage (cT1-T2N0) supraglottic squamous cell carcinomas: a systematic review.

Surgical oncology·2026
Same author

Lymphoid interstitial pneumonia in Sjögren disease: clinical course and comparison with other ILD patterns.

Rheumatology (Oxford, England)·2026

Related Experiment Video

Updated: Jun 27, 2025

Minimally Invasive Murine Laryngoscopy for Close-Up Imaging of Laryngeal Motion During Breathing and Swallowing
07:22

Minimally Invasive Murine Laryngoscopy for Close-Up Imaging of Laryngeal Motion During Breathing and Swallowing

Published on: December 1, 2023

485

An automated approach for real-time informative frames classification in laryngeal endoscopy using deep learning.

Chiara Baldini1,2, Muhammad Adeel Azam1,2, Claudio Sampieri3,4,5

  • 1Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy.

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
|May 2, 2024
PubMed
Summary

Artificial intelligence (AI) can automatically select informative frames from laryngoscopy videos. This AI system shows high accuracy and real-time capabilities, aiding in diagnosis and data management.

Keywords:
Artificial intelligenceDeep learningLaryngeal cancerLaryngoscopyLarynx

More Related Videos

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.5K
Mixed Reality Assisted Radical Endoscopic Thyroidectomy
08:06

Mixed Reality Assisted Radical Endoscopic Thyroidectomy

Published on: January 31, 2025

239

Related Experiment Videos

Last Updated: Jun 27, 2025

Minimally Invasive Murine Laryngoscopy for Close-Up Imaging of Laryngeal Motion During Breathing and Swallowing
07:22

Minimally Invasive Murine Laryngoscopy for Close-Up Imaging of Laryngeal Motion During Breathing and Swallowing

Published on: December 1, 2023

485
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.5K
Mixed Reality Assisted Radical Endoscopic Thyroidectomy
08:06

Mixed Reality Assisted Radical Endoscopic Thyroidectomy

Published on: January 31, 2025

239

Area of Science:

  • Otolaryngology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Laryngoscopy generates large video datasets, posing challenges for manual review and data extraction.
  • Automated informative image selection can improve data management and diagnostic accuracy in laryngoscopy.

Purpose of the Study:

  • To demonstrate the feasibility of an AI system for automatic, real-time selection of informative frames during laryngoscopy.
  • To provide visual feedback to otolaryngologists during examinations.

Main Methods:

  • Deep learning models, including ResNet-50, were trained and tested on internal and external datasets of laryngoscopy images (white light and narrow band).
  • The best-performing model's real-time performance was assessed using four testing videos.

Main Results:

  • The ResNet-50 model achieved high precision (95% internal, 97% external) and F1-scores (96% internal, 93% external).
  • The model demonstrated excellent performance in identifying diagnostically relevant frames.

Conclusions:

  • The AI model shows excellent performance for identifying key frames in laryngoscopic videos.
  • The system's accuracy and real-time capabilities make it promising for clinical use in quality control and AI-assisted tumor detection.