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

Abstract

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 = 0.70cm) and prospective dataset (mean error = 0.68cm) were noninferior to inter-radiologist variability (mean error = 0.70cm) within an equivalence bound of 0.1cm. 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.