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.

Classification of Aortic Dissection and Rupture on Post-contrast CT Images Using a Convolutional Neural Network.

Aortic dissections and ruptures are life-threatening injuries that must be immediately treated. Our national radiology practice receives dozens of these cases each month, but no automated process is currently available to check for critical pathologies before the images are opened by a radiologist. In this project, we developed a convolutional neural network model trained on aortic dissection and rupture data to assess the likelihood of these pathologies being present in prospective patients. This aortic injury model was used for study prioritization over the course of 4 weeks and model results were compared with clinicians' reports to determine accuracy metrics. The model obtained a sensitivity and specificity of 87.8% and 96.0% for aortic dissection and 100% and 96.0% for aortic rupture. We observed a median reduction of 395 s in the time between study intake and radiologist review for studies that were prioritized by this model. False-positive and false-negative data were also collected for retraining to provide further improvements in subsequent versions of the model. The methodology described here can be applied to a number of modalities and pathologies moving forward.