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Measurement of Endotracheal Tube Positioning on Chest X-Ray Using Object Detection


Patients who are intubated with endotracheal tubes often receive chest x-ray (CXR) imaging to determine whether the tube is correctly positioned. When these CXRs are interpreted by a radiologist, they evaluate whether the tube needs to be repositioned and typically provide a measurement in centimeters between the endotracheal tube tip and carina. In this project, a large dataset of endotracheal tube and carina bounding boxes was annotated on CXRs, and a machine-learning model was trained to generate these boxes on new CXRs and to calculate a distance measurement between the tube and carina. This model was applied to a gold standard annotated dataset, as well as to all prospective data passing through our radiology system for two weeks. Inter-radiologist variability was also measured on a test dataset. The distance measurements for both the gold standard dataset (mean error = 0.70 cm) and prospective dataset (mean error = 0.68 cm) were noninferior to inter-radiologist variability (mean error = 0.70 cm) within an equivalence bound of 0.1 cm. This suggests that this model performs at an accuracy similar to human measurements, and these distance calculations can be used for clinical report auto-population and/or worklist prioritization of severely malpositioned tubes.

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Data Availability

Data is in the author’s possession and is not publicly available.

Code Availability

Code is in the author’s possession and is not publicly available.


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Correspondence to Robert J. Harris Ph.D. .

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All authors are employed at the teleradiology practice described here.

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Harris, R.J., Baginski, S.G., Bronstein, Y. et al. Measurement of Endotracheal Tube Positioning on Chest X-Ray Using Object Detection. J Digit Imaging 34, 846–852 (2021).

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  • Endotracheal tube
  • Chest X-ray
  • Convolutional neural network
  • Bounding box
  • Machine learning