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

Evidence of Spin-Interference Effects in Exclusive J/Ļˆā†’e^{+}e^{-} Photoproduction in Ultraperipheral Heavy-Ion Collisions.

Physical review lettersĀ·2026
Same author

First Observation of Deuteron-Ī› Correlations at RHIC.

Physical review lettersĀ·2026
Same author

Observation of Charmonium Sequential Suppression in Heavy-Ion Collisions at the Relativistic Heavy Ion Collider.

Physical review lettersĀ·2026
Same author

Energy Independence of the Collins Asymmetry in p^{ā†‘}p Collisions.

Physical review lettersĀ·2026
Same author

Precision Measurement of Net-Proton-Number Fluctuations in Au+Au Collisions at RHIC.

Physical review lettersĀ·2025
Same author

Measurement of Two-Point Energy Correlators within Jets in p+p Collisions at sqrt[s]=200ā€‰ā€‰GeV.

Physical review lettersĀ·2025

Related Experiment Video

Updated: Jul 8, 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

513

Deep learning-based facial analysis for predicting difficult videolaryngoscopy: a feasibility study.

M Xia1, C Jin1, Y Zheng2

  • 1Department of Anaesthesiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Anaesthesia
|December 14, 2023
PubMed
Summary
This summary is machine-generated.

Artificial intelligence using facial images can predict difficult videolaryngoscopy. This AI model shows higher accuracy than traditional methods for airway management.

Keywords:
deep learningdifficult airwaydifficult laryngoscopyfacial analysisvideolaryngoscopy

More Related Videos

Point-of-Care Ultrasound: A Review of Ultrasound Parameters for Predicting Difficult Airways
08:21

Point-of-Care Ultrasound: A Review of Ultrasound Parameters for Predicting Difficult Airways

Published on: April 7, 2023

1.5K
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

Related Experiment Videos

Last Updated: Jul 8, 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

513
Point-of-Care Ultrasound: A Review of Ultrasound Parameters for Predicting Difficult Airways
08:21

Point-of-Care Ultrasound: A Review of Ultrasound Parameters for Predicting Difficult Airways

Published on: April 7, 2023

1.5K
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

Area of Science:

  • Anesthesiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Videolaryngoscopy improves tracheal intubation success but requires effective airway assessment.
  • Predicting difficult videolaryngoscopy remains crucial for patient safety.
  • Current assessment methods have limitations in predicting intubation difficulty.

Purpose of the Study:

  • To develop an artificial intelligence (AI) model for predicting difficult videolaryngoscopy.
  • To evaluate the performance of an AI facial analysis model against traditional methods.
  • To utilize neural networks for feature extraction from facial images.

Main Methods:

  • A neural network (ResNet-18) was used for facial image feature extraction.
  • Machine learning algorithms were employed to build predictive models.
  • Difficult videolaryngoscopy was defined as Cormack-Lehane grade 3 or 4.

Main Results:

  • The AI facial model achieved an area under the curve of 0.779 (95% CI: 0.733-0.825).
  • Sensitivity was 0.757 (95% CI: 0.650-0.845) and specificity was 0.721 (95% CI: 0.626-0.794).
  • The AI model significantly outperformed bedside examination and multivariate scores (p < 0.001).

Conclusions:

  • AI-based facial analysis is a viable method for predicting difficult videolaryngoscopy.
  • The developed AI model demonstrates superior predictive performance compared to conventional techniques.
  • This AI approach offers a promising advancement in airway management safety.