Mask R-CNN based multiclass segmentation model for endotracheal intubation using video laryngoscope.

Journal: Digital health
Published Date: Nov 6, 2023

Abstract

OBJECTIVE: Endotracheal intubation (ETI) is critical to secure the airway in emergent situations. Although artificial intelligence algorithms are frequently used to analyze medical images, their application to evaluating intraoral structures based on images captured during emergent ETI remains limited. The aim of this study is to develop an artificial intelligence model for segmenting structures in the oral cavity using video laryngoscope (VL) images.

Authors

  • Seung Jae Choi
    Transdisciplinary Department of Medicine and Advanced Technology, Seoul National University Hospital, Seoul, Republic of Korea.
  • Dae Kon Kim
    Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
  • Byeong Soo Kim
    Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, Republic of Korea.
  • Minwoo Cho
    Transdisciplinary Department of Medicine and Advanced Technology, Seoul National University Hospital, Seoul, Republic of Korea.
  • Joo Jeong
    Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
  • You Hwan Jo
    Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
  • Kyoung Jun Song
    Department of Emergency Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
  • Yu Jin Kim
    Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
  • Sungwan Kim
    Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Republic of Korea.

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