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

Digital Health, Telehealth and e-Innovation in Clinical Settings (401) Machine Learning (1465) Clinical Information and Decision Making (1407) Decision Support for Health Professionals (1185) Artificial Intelligence (1371) Respiratory Medicine (83) Tools, Programs and Algorithms (266)

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 (11) Tweetations (3) Metrics

Journals

  1. García-García F, Lee D, Mendoza-Garcés F, Irigoyen-Miró S, Legarreta-Olabarrieta M, García-Gutiérrez S, Arostegui I. Automated location of orofacial landmarks to characterize airway morphology in anaesthesia via deep convolutional neural networks. Computer Methods and Programs in Biomedicine 2023;232:107428 View
  2. Zhou C, Wang Y, Xue Q, Yang J, Zhu Y. Predicting difficult airway intubation in thyroid surgery using multiple machine learning and deep learning algorithms. Frontiers in Public Health 2022;10 View
  3. Senthilnathan M, Kundra P. Predictive Machine Learning Algorithms in Anticipating Problems with Airway Management. Airway 2023;6(1):4 View
  4. Naik N, Mathew P, Kundra P. Scope of artificial intelligence in airway management. Indian Journal of Anaesthesia 2024;68(1):105 View
  5. Dougherty J, Paxton J. Recent Technological Advances in Airway Management. Current Emergency and Hospital Medicine Reports 2024;12(1):32 View
  6. García-García F, Lee D, Mendoza-Garcés F, García-Gutiérrez S. Reliable prediction of difficult airway for tracheal intubation from patient preoperative photographs by machine learning methods. Computer Methods and Programs in Biomedicine 2024;248:108118 View
  7. De Rosa S, Bignami E, Bellini V, Battaglini D. The Future of Artificial Intelligence Using Images and Clinical Assessment for Difficult Airway Management. Anesthesia & Analgesia 2024 View
  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 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

Books/Policy Documents

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