• Micha Y Shamir, Dror Lederman, and Dietrich Gravenstein.
• Department of Anesthesiology, Perioperative Medicine and Pain Management, University of Miami Miller School of Medicine, Miami, FL, USA. shamir61@gmail.com
• Anesth. Analg.. 2011 Dec 1;113(6):1411-5.

BackgroundImproper endotracheal tube positioning carries a high risk for morbidity and mortality; verification and confirmation of correct placement is necessary. We propose a computer-automated identification of endotracheal tube positioning using image analysis. The end product will not retain a monitor; rather, the acquired image will be automatically analyzed by a mini electronic processor.MethodsAn algorithm that automatically analyzes images has been developed: it classifies images into esophagus, trachea, and carina. Image processing includes converting the image to grayscale and extracting and classifying into 1 class, on the basis of similarity to pretrained patterns. A prototypical video sensor mounted on an intubating stylet has also been assembled. This stylet was introduced into 10 bovine throats, and video images were gathered. Videos were analyzed and classified as carina, trachea, or esophagus. The videos were then introduced to the new algorithm. In each test cycle, 9 videos were used to train the algorithm, and the 10th was used as a benchmark. This procedure was repeated 10 times so that each video was used 9 times for teaching and 1 time for testing.ResultsTen videos were recorded, of which 1600 images were extracted (trachea: 490 images; carina: 550 images; and esophagus: 560 images). Only 1 esophageal image was classified as trachea (false positive 0.001%). Two carinal images and 22 tracheal images were recognized as esophagus (false negative 0.041%), sensitivity 0.98 and specificity 0.99. Twenty images of the carina were identified as trachea, and 25 images of the trachea were identified as the carina (false positive 0.045%, false negative 0.041%, sensitivity 0.96 and specificity 0.95).ConclusionA potential tube position verification system was assessed. High accuracy of the analysis algorithm was shown using nonperfused biological tissue, justifying further research.

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