Deep learning-based facial analysis for predicting difficult videolaryngoscopy: a feasibility study.

Journal: Anaesthesia
PMID: 38093485

Abstract

While videolaryngoscopy has resulted in better overall success rates of tracheal intubation, airway assessment is still an important prerequisite for safe airway management. This study aimed to create an artificial intelligence model to identify difficult videolaryngoscopy using a neural network. Baseline characteristics, medical history, bedside examination and seven facial images were included as predictor variables. ResNet-18 was introduced to recognise images and extract features. Different machine learning algorithms were utilised to develop predictive models. A videolaryngoscopy view of Cormack-Lehane grade of 1 or 2 was classified as 'non-difficult', while grade 3 or 4 was classified as 'difficult'. A total of 5849 patients were included, of whom 5335 had non-difficult and 514 had difficult videolaryngoscopy. The facial model (only including facial images) using the Light Gradient Boosting Machine algorithm showed the highest area under the curve (95%CI) of 0.779 (0.733-0.825) with a sensitivity (95%CI) of 0.757 (0.650-0.845) and specificity (95%CI) of 0.721 (0.626-0.794) in the test set. Compared with bedside examination and multivariate scores (El-Ganzouri and Wilson), the facial model had significantly higher predictive performance (p < 0.001). Artificial intelligence-based facial analysis is a feasible technique for predicting difficulty during videolaryngoscopy, and the model developed using neural networks has higher predictive performance than traditional methods.

Authors

  • M Xia
    Department of Anaesthesiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • C Jin
    Department of Radiation Oncology, Stanford University School of Medicine, Stanford California, USA.
  • Y Zheng
    State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China.
  • J Wang
    Joint Laboratory of Modern Agricultural Technology International Cooperation; Key Laboratory of Animal Production, Product Quality, and Security; College of Animal Science and Technology, Jilin Agricultural University, Changchun, China. moa4short@outlook.com.
  • M Zhao
    State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China.
  • S Cao
    Department of Anaesthesiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • T Xu
    Department of Anaesthesiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • B Pei
    Department of Anaesthesiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • M G Irwin
    Department of Anaesthesiology, University of Hong Kong, Hong Kong.
  • Z Lin
    3 Laboratory Animal Center, Wenzhou Medical University, Wenzhou, China.
  • H Jiang
    Department of Anaesthesiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

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