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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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Using Transfer Learning Method to Develop an Artificial Intelligence Assisted Triaging for Endotracheal Tube Position

Kuo-Ching Yuan1,2, Lung-Wen Tsai3, Kevin S Lai4

  • 1Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 10675, Taiwan.

Diagnostics (Basel, Switzerland)
|October 23, 2021
PubMed
Summary

An artificial intelligence system can automatically assess endotracheal tube (ETT) placement in chest X-rays (CXRs). This AI triage system shows promise for improving the accuracy and speed of confirming ETT positioning in intensive care units.

Keywords:
artificial intelligencechest X-rayendotracheal tubetransfer learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Critical Care Medicine

Background:

  • Endotracheal tube (ETT) placement is crucial for mechanical ventilation, but confirmation via chest X-ray (CXR) requires expert interpretation and can cause delays.
  • Improper ETT placement risks ventilation inefficiency and patient injury.

Purpose of the Study:

  • To develop and evaluate an artificial intelligence (AI)-based triage system for automated assessment of endotracheal tube (ETT) placement in chest X-rays (CXRs).

Main Methods:

  • Trained six AI models, including four pre-trained (VGG16, INCEPTION_V3, RESNET, DENSENET169) and two custom models, using transfer learning on 4293 CXRs from 2568 ICU patients.
  • Models were trained on regions of interest (ROIs) containing the clavicles, carina, and ETT tip, with CXRs labeled as "CORRECT" or "INCORRECT" ETT placement.

Main Results:

  • The VGG16 model, trained on ROIs, achieved acceptable performance with an AUROC of 92% and an F1 score of 0.87.
  • Other models did not achieve comparable performance levels.

Conclusions:

  • The study demonstrates the feasibility of using transfer learning for developing AI models to assess endotracheal tube placement in chest X-rays.
  • AI-powered assessment of ETT placement in CXRs holds potential for clinical application.