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An Artificial Intelligence System for Endotracheal Intubation Confirmation

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Proceedings of the 22nd Engineering Applications of Neural Networks Conference (EANN 2021)

Part of the book series: Proceedings of the International Neural Networks Society ((INNS,volume 3))

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Abstract

Complications related to the misplacement and dislodging of endotracheal intubation can be lethal. A novel artificial intelligence system for endotracheal intubation confirmation is described, based on the processing of images acquired during the intubation and identification of carina images. The system is comprised of a miniature metal oxide silicon sensor (CMOS) attached to the tip of a semi-rigid stylet connected to a digital signal processor (DSP) with an integrated video acquisition component. Video images are acquired and processed by a deep learning algorithm implemented on the DSP. The algorithm calculates the probability that the image belongs to the carina model that was a-priori trained and saved, and used for the classification decision. System performance was assessed on intubation videos recorded from 10 subjects who underwent general anesthesia during elective operations. The videos were annotated by an anesthesiologist, such that each video image was classified according to the anatomical position. A leave-one-case-out method was employed, such that in each iteration, images from one subject were used to train the system and estimate the carina model, and the remaining images were used to assess system performance. This process was repeated 10 times such that each subject participated once in testing. The results showed that this fully automatic image recognition system confirmed the correct tube positioning in all cases, for a 100% success rate.

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Author information

Authors and Affiliations

  1. Holon Institute of Technology, 84105, Holon, Israel

    Dror Lederman

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  1. Dror Lederman
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Correspondence to Dror Lederman .

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Editors and Affiliations

  1. School of Engineering, Department of Civil Engineering, Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Faculty of Applied Sciences, University of Sunderland, Sunderland, UK

    John Macintyre

  3. School of Computing and Digital Technologies, Teesside University, Middlesbrough, UK

    Chrisina Jayne

  4. University of the West of England, Bristol, UK

    Elias Pimenidis

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Lederman, D. (2021). An Artificial Intelligence System for Endotracheal Intubation Confirmation. In: Iliadis, L., Macintyre, J., Jayne, C., Pimenidis, E. (eds) Proceedings of the 22nd Engineering Applications of Neural Networks Conference. EANN 2021. Proceedings of the International Neural Networks Society, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-030-80568-5_11

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