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Related Experiment Video

Updated: Jul 8, 2025

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

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

Published on: December 1, 2023

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.

|December 14, 2023

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 learning difficult airway difficult laryngoscopy facial analysis videolaryngoscopy

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Last Updated: Jul 8, 2025

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

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Published on: December 1, 2023

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Published on: April 7, 2023

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

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Published on: April 14, 2023

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.