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 Experiment Video

Updated: May 31, 2026

An Automated Squint Method for Time-syncing Behavior and Brain Dynamics in Mouse Pain Studies
05:49

An Automated Squint Method for Time-syncing Behavior and Brain Dynamics in Mouse Pain Studies

Published on: November 1, 2024

Real-time face keypoint detection for pre-anesthetic assessment with optimized YOLO11 model based on DeBiFormer.

Daotong Wang1, Jianbo Su1

  • 1School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, China.

Frontiers in Medical Technology
|May 29, 2026
PubMed
Summary

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

Whole genome sequencing and comparative analysis of exopolysaccharide bioactivity from Lacticaseibacillus rhamnosus YT isolated from the feces of long-lived elderly individuals in Bama.

International journal of biological macromolecules·2025
Same author

Green manure application improves insect resistance of subsequent crops through the optimization of soil nutrients and rhizosphere microbiota.

iScience·2024
Same author

Gut Microbiota Affects Host Fitness of Fall Armyworm Feeding on Different Food Types.

Insects·2024
Same author

Polydopamine nanoparticles coated with a metal-polyphenol network for enhanced photothermal/chemodynamic cancer combination therapy.

International journal of biological macromolecules·2023
Same author

Antibacterial and antibiofilm potential of Lacticaseibacillus rhamnosus YT and its cell-surface extract.

BMC microbiology·2023
Same author

A Markerless 2D Video, Facial Feature Recognition-Based, Artificial Intelligence Model to Assist With Screening for Parkinson Disease: Development and Usability Study.

Journal of medical Internet research·2021

This study introduces an AI model for automated pre-anesthetic assessment, improving airway evaluation reliability. The YOLO11-Pose framework with DeBiFormer accurately measures keypoints for better patient safety.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Anesthesiology

Background:

  • Pre-anesthetic assessment is crucial for airway management but often lacks quantitative reliability.
  • Current methods for evaluating airway characteristics can be subjective and prone to variability.

Purpose of the Study:

  • To develop an automated, image-based framework for pre-anesthetic assessment using an optimized YOLO11-Pose model.
  • To quantitatively analyze facial and hand keypoints relevant to airway assessment, including mouth opening, thyromental distance, and neck mobility.

Main Methods:

  • Implementation of an optimized YOLO11-Pose framework integrated with a DeBiFormer module.
  • Single-stage inference for localizing key facial and hand landmarks.
  • Evaluation on a controlled dataset from Ruijin Hospital, employing Bland-Altman analysis, pixel-level error, and normalized mean error (NME).
Keywords:
YOLO11-Posedeep learningkeypoint detectionpose estimationpre-anesthetic assessment

Related Experiment Videos

Last Updated: May 31, 2026

An Automated Squint Method for Time-syncing Behavior and Brain Dynamics in Mouse Pain Studies
05:49

An Automated Squint Method for Time-syncing Behavior and Brain Dynamics in Mouse Pain Studies

Published on: November 1, 2024

Main Results:

  • High detection performance for key facial and hand points.
  • Reliable agreement with reference annotations, confirmed by Bland-Altman analysis and error metrics.
  • Successful Mallampati classification with 77.34% accuracy (4-class) and 83.99% accuracy (binary), kappa=0.65.

Conclusions:

  • The proposed AI method offers robust and clinically meaningful assessment of airway characteristics.
  • This automated approach has the potential to enhance the reliability and efficiency of pre-anesthetic evaluations in clinical practice.