Reliable prediction of difficult airway for tracheal intubation from patient preoperative photographs by machine learning methods.

Journal: Computer methods and programs in biomedicine
PMID: 38489935

Abstract

BACKGROUND: Estimating the risk of a difficult tracheal intubation should help clinicians in better anaesthesia planning, to maximize patient safety. Routine bedside screenings suffer from low sensitivity.

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.
  • Susana García-Gutiérrez
    Galdakao-Usansolo University Hospital, Research Unit - Galdakao, Basque Country, Spain. Electronic address: susana.garciagutierrez@osakidetza.eus.

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