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Using Deep Learning Segmentation for Endotracheal Tube Position Assessment.

William G Schultheis1, Paras Lakhani1,2

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Deep learning segmentation accurately measures endotracheal tube (ETT) position on chest x-rays (CXRs). The U-NET model demonstrated reliability comparable to radiologists in assessing ETT-carina distance.

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Accurate endotracheal tube (ETT) placement is crucial for patient care.
  • Assessing ETT position on chest x-rays (CXRs) typically relies on manual interpretation by radiologists.

Purpose of the Study:

  • To evaluate the efficacy of a deep learning segmentation model for determining endotracheal tube (ETT) position on frontal chest x-rays (CXRs).

Main Methods:

  • A U-NET deep learning architecture was developed to segment pixels belonging to the ETT and carina on 936 retrospective CXRs.
  • The model's performance was validated against measurements from two radiologists on internal and external test datasets.

Main Results:

  • The U-NET model achieved mean absolute differences of 0.60±0.61 cm (internal) and 0.48±0.47 cm (external) compared to radiologist measurements.
  • Interclass correlation coefficients were high, indicating excellent agreement: 0.87 (internal) and 0.92 (external).

Conclusions:

  • The U-NET deep learning model shows excellent reliability and performance for assessing endotracheal tube (ETT)-carina distance on CXRs.
  • This AI approach offers a viable, radiologist-level alternative for ETT position assessment.