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Related Concept Videos

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Related Experiment Video

Updated: May 24, 2026

The Rigid Tube as an Alternative in Controlling the Problematic Airway
08:26

The Rigid Tube as an Alternative in Controlling the Problematic Airway

Published on: June 6, 2020

An Explainable, Knowledge-Driven System for Difficult Airway Risk Stratification and Planning.

Akram Redjdal1,2, Adrien Ugon1,2, Fahira Abdoulaime2

  • 1Univ Gustave Eiffel, Aix-Marseille Univ, LBA, F-13015 Marseille, France.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

Recognizing difficult airways in surgery is vital. An ontology-based system standardizes assessment and recommends evidence-based plans, achieving 80% agreement on intubation techniques in simulations.

Keywords:
AnesthesiologyClinical Decision SupportClinical Practice GuidelinesIntensive Caresymbolic AI

Related Experiment Videos

Last Updated: May 24, 2026

The Rigid Tube as an Alternative in Controlling the Problematic Airway
08:26

The Rigid Tube as an Alternative in Controlling the Problematic Airway

Published on: June 6, 2020

Area of Science:

  • Anesthesiology and Perioperative Medicine
  • Medical Informatics
  • Artificial Intelligence in Healthcare

Background:

  • Difficult airway management is crucial for patient safety during anesthesia.
  • Current methods for identifying and escalating difficult airway cases can be inconsistent.
  • Standardized decision support is needed to improve airway planning accuracy.

Purpose of the Study:

  • To develop and evaluate an ontology-based clinical decision support system for pre-anesthesia difficult airway assessment.
  • To standardize the assessment process and provide evidence-based airway management plans.
  • To improve the consistency and reliability of difficult airway identification and planning.

Main Methods:

  • Developed an ontology modeling patient descriptors, predictive scores (e.g., ARNE score), and airway techniques.
  • Implemented a system to compute the ARNE score from recorded patient findings.
  • Utilized an expert-curated mapping for technique recommendations and evaluated the system on 20 simulated cases.

Main Results:

  • The system achieved 80% agreement on priority intubation techniques across simulated cases, including typical and edge presentations.
  • The decision support pipeline executed end-to-end, producing consistent and reasoner-validated outputs.
  • Demonstrated feasibility of an institution-aware system for airway planning.

Conclusions:

  • Ontology-based clinical decision support systems are feasible for standardizing difficult airway assessment.
  • The developed system provides transparent and evidence-based recommendations aligned with clinical practice.
  • This approach enhances patient safety by improving the consistency of airway planning.