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Detection of difficult airway using deep learning

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A Publisher Correction to this article was published on 27 February 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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Change history

  • 27 February 2020

    The articles listed below were published in Issue January 2020, Issue 1, instead of Issue February 2020, Issues 1–2.

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Authors and Affiliations

  1. Facultad de Ingeniería y Tecnología, Universidad de Montemorelos, Av. Libertad 1300 Poniente, Barrio Matamoros, 67530, Montemorelos, N.L., Mexico

    Kevin Aguilar & Germán H. Alférez

  2. Escuela de Medicina, Universidad de Montemorelos, Av. Libertad 1300 Poniente, Barrio Matamoros, 67530, Montemorelos, N.L., Mexico

    Christian Aguilar

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  1. Kevin Aguilar
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  2. Germán H. Alférez
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  3. Christian Aguilar
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Correspondence to Germán H. Alférez.

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Aguilar, K., Alférez, G.H. & Aguilar, C. Detection of difficult airway using deep learning. Machine Vision and Applications 31, 4 (2020). https://doi.org/10.1007/s00138-019-01055-3

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