Using Deep Learning Segmentation for Endotracheal Tube Position Assessment.

PURPOSE: The purpose of this study was to determine the efficacy of using deep learning segmentation for endotracheal tube (ETT) position on frontal chest x-rays (CXRs).

MATERIALS AND

METHODS

This was a retrospective trial involving 936 deidentified frontal CXRs divided into sets for training (676), validation (50), and 2 for testing (210). This included an "internal test" set of 100 CXRs from the same institution, and an "external test" set of 110 CXRs from a different institution. Each image was labeled by 2 radiologists with the ETT-carina distance. On the training images, 1 radiologist manually segmented the ETT tip and inferior wall of the carina. A U-NET architecture was constructed to label each pixel of the CXR as belonging to either the ETT, carina, or neither. This labeling allowed the distance between the ETT and carina to be compared with the average of 2 radiologists. The interclass correlation coefficients, mean, and SDs of the absolute differences between the U-NET and radiologists were calculated.

RESULTS

The mean absolute differences between the U-NET and average of radiologist measurements were 0.60±0.61 and 0.48±0.47 cm on the internal and external datasets, respectively. The interclass correlation coefficients were 0.87 (0.82, 0.91) and 0.92 (0.88, 0.94) on the internal and external datasets, respectively.

CONCLUSION

The U-NET model had excellent reliability and performance similar to radiologists in assessing ETT-carina distance.