Enhancing nasal endoscopy: Classification, detection, and segmentation of anatomic landmarks using a convolutional neural network.

Journal: International forum of allergy & rhinology
PMID: 38853655

Abstract

A convolutional neural network (CNN)-based model can accurately localize and segment turbinates in images obtained during nasal endoscopy (NE). This model represents a starting point for algorithms that comprehensively interpret NE findings.

Authors

  • Vinayak Ganeshan
    Department of Otorhinolaryngology, Ochsner Health, New Orleans, Louisiana, USA.
  • Jonathan Bidwell
    Department of Otorhinolaryngology, Ochsner Health, New Orleans, Louisiana, USA.
  • Dipesh Gyawali
    Department of Otorhinolaryngology, Ochsner Health, New Orleans, Louisiana, USA.
  • Thinh S Nguyen
    Department of Otorhinolaryngology, Ochsner Health, New Orleans, Louisiana, USA.
  • Jonathan Morse
    Department of Otorhinolaryngology, Ochsner Health, New Orleans, Louisiana, USA.
  • Madeline P Smith
    Ochsner Clinical School, University of Queensland, New Orleans, Louisiana, USA.
  • Blair M Barton
    Department of Otorhinolaryngology, Ochsner Health, New Orleans, Louisiana, USA.
  • Edward D McCoul
    Department of Otorhinolaryngology, Ochsner Clinic, New Orleans, Louisiana, USA.

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