Machine Learning in Laryngoscopy Analysis: A Proof of Concept Observational Study for the Identification of Post-Extubation Ulcerations and Granulomas.

Journal: The Annals of otology, rhinology, and laryngology
PMID: 32795159

Abstract

OBJECTIVE: Computer-aided analysis of laryngoscopy images has potential to add objectivity to subjective evaluations. Automated classification of biomedical images is extremely challenging due to the precision required and the limited amount of annotated data available for training. Convolutional neural networks (CNNs) have the potential to improve image analysis and have demonstrated good performance in many settings. This study applied machine-learning technologies to laryngoscopy to determine the accuracy of computer recognition of known laryngeal lesions found in patients post-extubation.

Authors

  • Felix Parker
    Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
  • Martin B Brodsky
    Department of Physical Medicine and Rehabilitation, Johns Hopkins University, Baltimore, MD, USA.
  • Lee M Akst
    Department of Otolaryngology - Head and Neck Surgery, Johns Hopkins University, Baltimore, MD, USA.
  • Haider Ali
    Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.

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