Automatic endotracheal tube position confirmation system based on image classification – A preliminary assessment

Endotracheal intubation is a complex medical procedure in which a ventilating tube is inserted into the human trachea. Improper positioning carries potentially fatal consequences and therefore confirmation of correct positioning is mandatory. This paper introduces a novel system for endotracheal tube position confirmation. The proposed system comprises a miniature complementary metal oxide silicon sensor (CMOS) attached to the tip of a semi rigid stylet and connected to a digital signal processor (DSP) with an integrated video acquisition component. Video signals are acquired and processed by a confirmation algorithm implemented on the processor. The confirmation approach is based on video image classification, i.e., identifying desired expected anatomical structures (upper trachea and main bifurcation of the trachea) and undesired structures (esophagus). The desired and undesired images are indicators of correct or incorrect endotracheal tube positioning. The proposed methodology is comprised of a continuous and probabilistic image representation scheme using Gaussian mixture models (GMMs), estimated using a greedy algorithm. A multi-dimensional feature space, which consists of several textural-based features, is utilized to represent the images. The performance of the proposed algorithm was evaluated using two datasets: a dataset of 1600 images extracted from 10 videos recorded during intubations on dead cows, and a dataset of 358 images extracted from 8 videos recorded during intubations performed on human subjects. Each one of the video images was classified by a medical expert into one of three categories: upper tracheal intubation, correct (carina) intubation and esophageal intubation. The results, obtained using a leave-one-case-out method, show that the system correctly classified 1530 out of 1600 (95.6%) of the cow intubations images, and 351 out of the 358 human images (98.0%). Misclassification of an image of the esophagus as carina or upper-trachea, which is potentially fatal, was extremely rare (only one case when in the animal dataset and no cases when in the human intubation dataset). The classification results of the cow intubations dataset compare favorably with a state-of-the-art classification method tested on the same dataset.