Automated location of orofacial landmarks to characterize airway morphology in anaesthesia via deep convolutional neural networks.

Journal: Computer methods and programs in biomedicine
Published Date: Feb 25, 2023

Abstract

BACKGROUND: A reliable anticipation of a difficult airway may notably enhance safety during anaesthesia. In current practice, clinicians use bedside screenings by manual measurements of patients' morphology.

Authors

  • Fernando García-García
    Basque Center for Applied Mathematics (BCAM) - Bilbao, Basque Country, Spain. Electronic address: fegarcia@bcamath.org.
  • Dae-Jin Lee
    Basque Center for Applied Mathematics (BCAM) - Bilbao, Basque Country, Spain; IE University, School of Science and Technology - Madrid, Madrid, Spain. Electronic address: daejin.lee@ie.edu.
  • Francisco J Mendoza-Garcés
    Galdakao-Usansolo University Hospital, Anaesthesia & Resuscitation Service - Galdakao, Basque Country, Spain. Electronic address: franciscojavier.mendozagarces@osakidetza.eus.
  • Sofía Irigoyen-Miró
    Galdakao-Usansolo University Hospital, Anaesthesia & Resuscitation Service - Galdakao, Basque Country, Spain. Electronic address: sofia.irigoyenmiro@osakidetza.eus.
  • María J Legarreta-Olabarrieta
    Galdakao-Usansolo University Hospital, Research Unit - Galdakao, Basque Country, Spain. Electronic address: mariajose.legarretaolabarrieta@osakidetza.eus.
  • Susana García-Gutiérrez
    Galdakao-Usansolo University Hospital, Research Unit - Galdakao, Basque Country, Spain. Electronic address: susana.garciagutierrez@osakidetza.eus.
  • Inmaculada Arostegui
    Basque Center for Applied Mathematics (BCAM) - Bilbao, Basque Country, Spain; University of the Basque Country (UPV/EHU), Department of Mathematics - Leioa, Basque Country, Spain. Electronic address: inmaculada.arostegui@ehu.eus.

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