Abstract
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. In this paper we report the results of using a neural network-based image classification 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 performance of the proposed algorithm was evaluated using two datasets: a dataset of 250 images of the upper airways. The results, obtained using a leave-one-case-out method, show that the system correctly classified 240 out of 250 (96.0%).
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Lederman, D. (2015). Endotracheal Tube Position Confirmation System Using Neural Networks. In: Iliadis, L., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2015. Communications in Computer and Information Science, vol 517. Springer, Cham. https://doi.org/10.1007/978-3-319-23983-5_8
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DOI: https://doi.org/10.1007/978-3-319-23983-5_8
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