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Point-of-Care Ultrasound in Airway Management.

Daniele Salvatore Paternò1, Luigi La Via2, Emilia Lo Giudice3,4

  • 1Department of Anaesthesia and Intensive Care, "Giovanni Paolo II" Hospital, 97100 Ragusa, Italy.

Journal of Clinical Medicine
|April 14, 2026
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Summary

Point-of-Care Ultrasound (POCUS) improves airway assessment and can guide emergency procedures. Integrating POCUS with AI may personalize airway management, but standardized training is crucial for its widespread adoption.

Keywords:
POCUSairway managementartificial intelligencecricothyrotomydifficult airwayultrasound

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Area of Science:

  • Anesthesiology
  • Medical Imaging
  • Emergency Medicine

Background:

  • Difficult airways are a major cause of anesthesia complications, with limited traditional predictors.
  • Point-of-Care Ultrasound (POCUS) offers non-invasive, real-time airway visualization and measurement.
  • This review synthesizes evidence on POCUS for airway evaluation.

Purpose of the Study:

  • To review the current evidence on POCUS as an adjunct for airway evaluation.
  • To explore POCUS's role in sonoanatomy, predicting difficult airways, emergency access, and tube placement verification.
  • To discuss AI integration and training needs for POCUS in airway management.

Main Methods:

  • Systematic literature search of PubMed/MEDLINE, Scopus, and Web of Science.
  • Included English-language peer-reviewed studies on sonographic airway assessment.
  • Focused on sonoanatomy, prediction, emergency guidance, and tube confirmation.

Main Results:

  • POCUS enhances visualization of airway anatomy and aids risk stratification.
  • Ultrasound can provide real-time guidance during emergency airway procedures.
  • AI integration shows promise in diagnostic performance for airway assessment.

Conclusions:

  • Airway ultrasound may lead to more personalized and safer airway management.
  • Standardized training protocols and validation in varied settings are essential.
  • Future research should focus on integrating POCUS into guidelines via evidence-based algorithms.