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Optical Biopsy: Automated Classification of Airway Endoscopic Findings Using a Convolutional Neural Network.

Authors
  • Dunham, Michael E1
  • Kong, Keonho A1
  • McWhorter, Andrew J1
  • Adkins, Lacey K1
  • 1 Department of Otolaryngology-Head and Neck Surgery, School of Medicine, Louisiana State University Health Sciences Center, New Orleans, Louisiana, U.S.A.
Type
Published Article
Journal
The Laryngoscope
Publisher
Wiley (John Wiley & Sons)
Publication Date
Feb 01, 2022
Volume
132 Suppl 4
Identifiers
DOI: 10.1002/lary.28708
PMID: 32343434
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Create an autonomous computational system to classify endoscopy findings. Computational analysis of vocal fold images at an academic, tertiary-care laryngology practice. A series of normal and abnormal vocal fold images were obtained from the image database of an academic tertiary care laryngology practice. The benign images included normals, nodules, papilloma, polyps, and webs. A separate set of carcinoma and leukoplakia images comprised a single malignant-premalignant class. All images were classified with their existing labels. Images were randomly withheld from each class for testing. The remaining images were used to train and validate a neural network for classifying vocal fold lesions. Two classifiers were developed. A multiclass system classified the five categories of benign lesions. A separate analysis was performed using a binary classifier trained to distinguish malignant-premalignant from benign lesions. Precision ranged from 71.7% (polyps) to 89.7% (papilloma), and recall ranged from 70.0% (papilloma) to 88.0% (nodules) for the benign classifier. Overall accuracy for the benign classifier was 80.8%. The binary classifier correctly identified 92.0% of the malignant-premalignant lesions with an overall accuracy of 93.0%. Autonomous classification of endoscopic images with artificial intelligence technology is possible. Better network implementations and larger datasets will continue to improve classifier accuracy. A clinically useful optical cancer screening system may require a multimodality approach that incorporates nonvisual spectra. NA Laryngoscope, 132:S1-S8, 2022. © 2020 The American Laryngological, Rhinological and Otological Society, Inc.

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