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Airway management is essential in emergency and surgical medicine, ensuring ventilation and oxygenation in patients who cannot maintain their own airway. Clinicians use a range of techniques and devices to secure the airway, depending on the patient’s condition and the clinical context. Key methods include endotracheal intubation, rapid sequence intubation (RSI), supraglottic airway devices, and advanced visualization aids. In cases where these approaches fail, surgical airway...
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Updated: Jan 9, 2026

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

Meruyert Alatau1, Johann Bauer2, Vitaliy Sazonov3,4

  • 1Department of Medicine, School of Medicine, Nazarbayev University, Kerey Zhanibek Handar Street 5/1, Astana 010000, Kazakhstan.

Journal of Clinical Medicine
|December 11, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) and machine learning (ML) models show superior accuracy in predicting difficult airways compared to traditional methods. The MixMatch deep learning model achieved the highest performance, highlighting AI

Keywords:
airway managementartificial intelligencedifficult airwayintubation predictionmachine learning

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Area of Science:

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

Background:

  • Accurate difficult airway prediction is critical for patient safety in anesthesiology and emergency medicine.
  • Traditional assessment tools often lack sufficient sensitivity and specificity.
  • Artificial intelligence (AI) and machine learning (ML) offer promising advancements for airway assessment.

Purpose of the Study:

  • Evaluate the performance of AI/ML models for difficult airway prediction.
  • Compare AI/ML models against traditional clinical methods.
  • Analyze the methodological robustness, clinical applicability, and ethical considerations of AI/ML models.

Main Methods:

  • Comprehensive literature search (PubMed, Web of Science, Scopus) for studies from 2020-2025.
  • Inclusion of original research and review articles on AI/ML for difficult airway prediction.
  • Extraction and comparison of key metrics (AUC, sensitivity, specificity) and qualitative analysis of study characteristics.

Main Results:

  • AI models significantly outperformed traditional assessment tools.
  • The MixMatch semi-supervised deep learning (DL) model demonstrated highest performance (AUC 0.9435, sensitivity 89.58%, specificity 90.13%).
  • AI models using facial imaging and DL excelled over those using only clinical parameters, though methodological heterogeneity and limited diversity were noted.

Conclusions:

  • AI and ML models hold significant potential to revolutionize difficult airway assessment.
  • These models can enhance diagnostic accuracy and provide real-time clinical decision support.
  • Addressing methodological standardization, population diversity, interpretability, and ethical concerns is crucial for clinical implementation.