Endotracheal tube intubation is often used when patients are ill and require respiratory assistance. These tubes must be positioned properly in relation to the carina; too high and the lungs may not be respirated, too low and only one lung may be respirated. Our institution receives approximately 4,000 XR Chest images every day, 5% of which contain an endotracheal tube. If the tube is determined to be malpositioned by the reading radiologist, this information is relayed back to the site for tube adjustment. We hypothesized that by training a convolution neural net using annotations of Chest XR images, we could localize both the endotracheal tube and the carina on prospective Chest XR data and use this information to classify images as having a malpositioned tube or not, along with the distance in cm that the tube must be adjusted if malpositioned.