Detection, localization, and measurement of endotracheal tube positioning on adults' chest X-ray: developing a prediction model.
Authors
Affiliations (6)
Affiliations (6)
- Department of Radiology and Nuclear Medicine, Faculty of Medical Sciences in Katowice, Medical University of Silesia, 40-752, Katowice, Poland. [email protected].
- Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland. [email protected].
- Students' Scientific Association of Computer Analysis and Artificial Intelligence at the Department of Radiology and Nuclear Medicine of the Medical University of Silesia in Katowice, Katowice, Poland.
- Department of Anesthesiology and Intensive Therapy, Provincial Hospital in Bielsko-Biała, A Branch of Silesian Medical University, Katowice, Poland.
- Department of Radiology and Nuclear Medicine, Faculty of Medical Sciences in Katowice, Medical University of Silesia, 40-752, Katowice, Poland.
- Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland.
Abstract
Accurate placement of the endotracheal tube (ETT) is critical for ensuring optimal care for patients requiring mechanical ventilation and preventing potential complications. ETT positioning can be assessed using several methods, with chest X-ray (CXR) being the most precise. Radiologists evaluate whether the ETT requires adjustment by measuring the distance between the distal tip of the ETT and the tracheal carina. This study presents the development of a machine learning model to detect and measure ETT position on adult CXRs and evaluates its performance. Six physicians annotated ETT and trachea locations on a dataset of 3856 CXRs. The U-Net-based model was then trained to generate trachea and ETT segmentations. After post-processing steps, an estimate of the distance between the distal tip of the ETT and the tracheal carina was found. It was demonstrated that the trained model is capable of estimating the position of the ETT and calculating the distance from the tube tip to the tracheal carina. The Dice index for the segmentations on the external validation subset for the trachea and ETT was 89.2% ± 9.0% and 87.8% ± 16.9%, respectively. The estimated absolute error on the external validation subset was 4.72 mm. This model represents a promising tool to support clinicians, particularly in Intensive Care Units, where correct intubation and effective ventilation are critical. It may also be integrated into clinical workflows to facilitate patient management and enhance patient safety.