Computer-Aided Diagnosis for Endotracheal Intubation Confirmation using Video-image Classification

In this paper, a Computer-Aided Diagnosis (CAD) system for endotracheal tube position confirmation, and detection of errors in intubation positioning is presented. Endotracheal intubation is a complex procedure which requires high skills and the use of secondary confirmation devices to ensure correct positioning of the tube. Our novel confirmation approach is based on video images classification and specifically on identification of specific anatomical landmarks, including esophagus, upper trachea and main bifurcation of the trachea into the two primary bronchi ("carina"), as indicators of correct or incorrect tube insertion and positioning. Classification of the images is performed using a neural network classifier. The performance of the proposed approach was evaluated using a dataset of cow-intubation videos and a dataset of human-intubation videos. Each one of the video images was manually (visually) classified by a medical expert into one of three categories: upper tracheal intubation, correct (carina) intubation and esophageal intubation. The image classification algorithm was applied off-line using a leave-one-case-out method. The results show that the system correctly classified 1567 out of 1600 (97.9%) of the cow intubations images, and 349 out of the 358 human intubations images (97.5%).