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Deep learning accurately assessed endotracheal tube (ETT) position on chest radiographs, showing excellent agreement with radiologists. The AI model demonstrated high sensitivity and specificity in detecting ETT-carina distance, improving radiograph analysis.

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Accurate endotracheal tube (ETT) placement is critical for patient care.
  • Radiographic assessment of ETT position is standard but can be subjective.
  • Deep learning offers potential for automated and objective image analysis.

Purpose of the Study:

  • To evaluate the efficacy of a deep learning model in assessing endotracheal tube (ETT) position on chest radiographs.
  • To compare the performance of deep learning with radiologist assessments of ETT-carina distance.

Main Methods:

  • A retrospective analysis of 22,960 chest radiographs from 11,153 patients was performed.
  • An Inception V3 deep neural network was trained to predict endotracheal tube (ETT)-carina distance.
  • The model's performance was evaluated against radiologists using intraclass correlation coefficients (ICCs), sensitivity, and specificity on internal and external test datasets.

Main Results:

  • Deep learning achieved an ICC of 0.84 (internal) and 0.89 (external) for ETT-carina distance, comparable to radiologists (0.93 internal, 0.84 external).
  • The AI model demonstrated 93.9% sensitivity and 97.7% specificity for detecting ETT-carina distances less than 1 cm.
  • The deep learning model predicted ETT-carina distance within 1 cm in most cases.

Conclusions:

  • Deep learning models can effectively and accurately assess endotracheal tube (ETT) position on radiographs.
  • The AI system exhibits excellent interrater agreement with radiologists.
  • The model shows high sensitivity and specificity, particularly for identifying low ETT positions.