A Convolutional Neural Network for Real Time Classification, Identification, and Labelling of Vocal Cord and Tracheal Using Laryngoscopy and Bronchoscopy Video.

Journal: Journal of medical systems
Published Date: Jan 2, 2020

Abstract

BACKGROUND: The use of artificial intelligence, including machine learning, is increasing in medicine. Use of machine learning is rising in the prediction of patient outcomes. Machine learning may also be able to enhance and augment anesthesia clinical procedures such as airway management. In this study, we sought to develop a machine learning algorithm that could classify vocal cords and tracheal airway anatomy real-time during video laryngoscopy or bronchoscopy as well as compare the performance of three novel convolutional networks for detecting vocal cords and tracheal rings.

Authors

  • Clyde Matava
    Department of Anesthesia and Pain Medicine, The Hospital for Sick Children, Toronto 555 University Avenue, Toronto, ON, M5G 1X8, Canada. clyde.matava@sickkids.ca.
  • Evelina Pankiv
    Department of Anesthesia and Pain Medicine, The Hospital for Sick Children, Toronto 555 University Avenue, Toronto, ON, M5G 1X8, Canada.
  • Sam Raisbeck
    Department of Anesthesia and Pain Medicine, The Hospital for Sick Children, Toronto 555 University Avenue, Toronto, ON, M5G 1X8, Canada.
  • Monica Caldeira
    Department of Anesthesia and Pain Medicine, The Hospital for Sick Children, Toronto 555 University Avenue, Toronto, ON, M5G 1X8, Canada.
  • Fahad Alam
    Collaborative Human Immersive Interactive (CHISIL) Laboratory, The Hospital for Sick Children Toronto and Sunnybrook Health Sciences, Toronto, Ontario, Canada.

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