This paper is in the following e-collection/theme issue:

Digital Health, Telehealth and e-Innovation in Clinical Settings (474) Machine Learning (1728) Clinical Information and Decision Making (1642) Decision Support for Health Professionals (1336) Artificial Intelligence (1714) Respiratory Medicine (120) Tools, Programs and Algorithms (320)

Published on in Vol 11, No 1 (2022): Jan-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28366, first published .
Machine Learning Approaches for Predicting Difficult Airway and First-Pass Success in the Emergency Department: Multicenter Prospective Observational Study

Machine Learning Approaches for Predicting Difficult Airway and First-Pass Success in the Emergency Department: Multicenter Prospective Observational Study

Machine Learning Approaches for Predicting Difficult Airway and First-Pass Success in the Emergency Department: Multicenter Prospective Observational Study

Authors of this article:

Syunsuke Yamanaka1 Author Orcid Image ;   Tadahiro Goto2 Author Orcid Image ;   Koji Morikawa3 Author Orcid Image ;   Hiroko Watase4 Author Orcid Image ;   Hiroshi Okamoto5 Author Orcid Image ;   Yusuke Hagiwara6 Author Orcid Image ;   Kohei Hasegawa7 Author Orcid Image
Article Authors Cited by (17) Tweetations (3) Metrics

Journals

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  8. Kim J, Jung H, Lee S, Hou J, Kwon Y. Improving difficult direct laryngoscopy prediction using deep learning and minimal image analysis: a single-center prospective study. Scientific Reports 2024;14(1) View
  9. Liao Z, Mathur N, Joshi V, Joshi S. The Promise of Artificial Intelligence in Neuroanesthesia: An Update. Journal of Neuroanaesthesiology and Critical Care 2024;11(03):167 View
  10. Kim J, Han S, Hwang S, Lee J, Kwon Y. Machine Learning Predictions and Identifying Key Predictors for Safer Intubation: A Study on Video Laryngoscopy Views. Journal of Personalized Medicine 2024;14(9):902 View
  11. Sezari P, Kohzadi Z, Dabbagh A, Jafari A, Khoshtinatan S, Mottaghi K, Kohzadi Z, Rahmatizadeh S. Unravelling intubation challenges: a machine learning approach incorporating multiple predictive parameters. BMC Anesthesiology 2024;24(1) View
  12. Thiebaud P. Gestion des voies aériennes en médecine d’urgence. EMC - Médecine d 'urgence 2023;17(3):1 View
  13. Зайцев А, Сорокин А, Зайцев Ю, Дубровин К, Усикян Э. Роль искусственного интеллекта в прогнозировании трудных дыхательных путей у взрослых: обзор литературы. Annals of Critical Care 2025;(1):110 View
  14. Srivilaithon W, Thanasarnpaiboon P. Performance of machine learning models in predicting difficult laryngoscopy in the emergency department: a single-centre retrospective study comparing with conventional regression method. BMC Emergency Medicine 2025;25(1) View
  15. Wilk M, Pikiewicz W, Florczak K, Jakóbczak D. Use of Artificial Intelligence in Difficult Airway Assessment: The Current State of Knowledge. Journal of Clinical Medicine 2025;14(5):1602 View
  16. Guo M, Hou Y, Liu Y, Yang B, Qiao C, Li J. From algorithms to airways: Applying artificial intelligence to enhance airway assessment, management, and training. Trends in Anaesthesia and Critical Care 2025;61:101548 View

Books/Policy Documents

  1. Wu C, Mathur P. Artificial Intelligence in Clinical Practice. View