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Machine Vision and Applications
Published in: Machine Vision and Applications 1-2/2020

01-02-2020 | Original Article

Detection of difficult airway using deep learning

Authors: Kevin Aguilar, Germán H. Alférez, Christian Aguilar

Published in: Machine Vision and Applications | Issue 1-2/2020

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Abstract

Whenever a patient needs to enter the operating room, in case the surgery requires general anesthesia, he/she must be intubated, and an anesthesiologist has to make a previous check to the patient in order to evaluate his/her airway. This process should be done to the patient to anticipate any problem, such as a difficult airway at the time of being anesthetized. In fact, the inadequate detection of a difficult airway can cause serious complications, even death. This research work proposes a mobile app that uses a convolutional neural network to detect a difficult airway. This model classifies two classes of the Mallampati score, namely Mallampati 1–2 (with low risk of difficult airway) and Mallampati 3–4 (with higher risk of difficult airway). The average accuracy of the predictive model is 88.5% for classifying pictures. A total of 240 pictures were used for training the model. The results of sensitivity and specificity were 90% in average.
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Metadata
Title
Detection of difficult airway using deep learning
Authors
Kevin Aguilar
Germán H. Alférez
Christian Aguilar
Publication date
01-02-2020
Publisher
Springer Berlin Heidelberg
Published in
Machine Vision and Applications / Issue 1-2/2020
Print ISSN: 0932-8092
Electronic ISSN: 1432-1769
DOI
https://doi.org/10.1007/s00138-019-01055-3

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