An endotracheal intubation confirmation system based on carina image detection: a proof of concept.

Publisher: Springer Country of Publication: United States NLM ID: 7704869 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1741-0444 (Electronic) Linking ISSN: 01400118 NLM ISO Abbreviation: Med Biol Eng Comput Subsets: MEDLINE

Publication: New York, NY : Springer
Original Publication: Stevenage, Eng., Peregrinus.

Intubation, Intratracheal/*methods ; Trachea/*anatomy & histology; Algorithms ; Esophagus ; Foreign Bodies/diagnosis ; Foreign Bodies/etiology ; Humans ; Intubation, Intratracheal/adverse effects ; Manikins ; Video Recording/methods

In this paper, a novel system for automatic confirmation of endotracheal intubation is proposed. The system comprises a miniature CMOS sensor and electric wires attached to a rigid stylet. Video signals are continuously acquired and processed by the algorithm implemented on a PC/DSP. The system is based on detection of the carina image as an anatomical landmark of correct tube positioning and it thus utilizes direct visual cues. Detection of the carina is performed based on unsupervised clustering, using a greedy-Gaussian mixture framework. The performance of the proposed system was initially evaluated using a mannequin model. A scientific prototype was assembled and used to perform repeated intubations on the model and collect a database of video signals which were processed off-line. The videos were categoried by a medical professional into carina, upper-trachea, and esophagus. An accuracy of 100% was achieved in discriminating between the carina and other anatomical structures including esophagus and upper-trachea. As an additional validation, the system was tested using a dataset of 231 video images recorded from five human subjects during intubation. The system correctly classified 120 out of 125 non-carina images (i.e. a sensitivity of 96.0%), and 100 out of 106 carina images (i.e. a specificity 94.3%). Using a 10th-order median filter, applied on the frame-based classification results, a 100% accuracy rate was obtained.

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Date Created: 20100930 Date Completed: 20111205 Latest Revision: 20211020

20221213

10.1007/s11517-010-0680-4

20878551