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An ultrasound-based artificial intelligence framework for difficult airway prediction: A two-model, three-step

Chunmeng Fu1, Cunyuan Luan2, Huabo Liu2,3

  • 1Department of Anesthesiology, The Affiliated Hospital of Qingdao University, Qingdao, China.

Plos One
|February 18, 2026
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Summary

This study introduces an AI framework using ultrasound for difficult airway prediction. The developed models show promise in identifying patients at risk, aiding clinical decisions.

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Area of Science:

  • Anesthesiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Difficult airway management presents significant clinical challenges.
  • Ultrasound parameters and deep learning show potential for difficult airway assessment.
  • An AI framework for difficult airway prediction is needed.

Purpose of the Study:

  • To develop and internally validate an ultrasound-based AI framework for predicting difficult airways.
  • To construct a "two-model, three-step" hierarchical strategy for risk stratification.
  • To evaluate the performance of AI models in difficult airway prediction.

Main Methods:

  • A cohort of 903 patients undergoing elective general anesthesia was studied.
  • Two convolutional neural network models, CL-AI and VIDIAC-AI, were developed using neck ultrasound images.
  • Model performance was assessed using cross-validation and an independent test set.

Main Results:

  • Difficult laryngoscopy occurred in 20.9% (direct) and 5.5% (video) of patients.
  • The CL-AI model achieved an AUC of 0.86 and accuracy of 0.84.
  • The VIDIAC-AI model achieved an AUC of 0.82 and accuracy of 0.81.

Conclusions:

  • An ultrasound-based AI framework was developed for difficult airway risk stratification.
  • The "Two-Model, Three-Step" framework serves as a clinical decision-support tool.
  • Further validation in large multicenter cohorts is required.