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Validation of a Deep Learning-based Automatic Detection Algorithm for Measurement of Endotracheal Tube-to-Carina

Min-Hsin Huang1, Chi-Yeh Chen2, Ming-Huwi Horng3

  • 1Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.

Anesthesiology
|September 21, 2022
PubMed
Summary
This summary is machine-generated.

A new deep learning algorithm accurately detects endotracheal tube (ETT) tip and carina on chest X-rays. This AI tool matches or surpasses critical care clinicians in measuring ETT-carina distance, improving patient safety.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Critical Care Medicine

Background:

  • Improper endotracheal tube (ETT) positioning is a common and dangerous issue in intensive care units.
  • Accurate ETT placement is crucial for effective ventilation and preventing complications.

Purpose of the Study:

  • To develop and evaluate a deep learning algorithm for automatic detection of ETT tip and carina on portable chest radiographs.
  • To compare the algorithm's accuracy in ETT-carina distance measurement against frontline critical care clinicians.

Main Methods:

  • A deep learning algorithm was trained on 1,842 chest radiographs from intubated adult patients.
  • The algorithm's performance was validated using cross-validation, external datasets, and an observer performance test involving 11 clinicians.
  • Key metrics included errors in ETT tip detection, carina detection, and ETT-carina distance measurement.

Main Results:

  • The algorithm demonstrated median errors of 3.9 mm and 4.2 mm in ETT-carina distance during cross-validation and external validation, respectively.
  • In observer tests, the algorithm achieved median errors of 2.6 mm (ETT tip), 3.6 mm (carina), and 4.0 mm (distance), outperforming multiple clinicians.
  • The algorithm showed superior accuracy in achieving specific error margins (5mm, 10mm, 15mm) compared to clinicians across all tested parameters.

Conclusions:

  • A deep learning-based algorithm can accurately detect ETT tip and carina on radiographs.
  • The developed algorithm demonstrates performance comparable to or exceeding that of critical care clinicians in ETT positioning assessment.