Automatic Recognition of Laryngoscopic Images Using a Deep-Learning Technique.
Adult
Deep Learning
/ statistics & numerical data
Female
Humans
Image Interpretation, Computer-Assisted
/ statistics & numerical data
Laryngeal Neoplasms
/ diagnostic imaging
Laryngoscopy
/ methods
Male
Otolaryngologists
/ statistics & numerical data
Reproducibility of Results
Retrospective Studies
Sensitivity and Specificity
Deep learning
artificial intelligence
clinical visual assessment.
convolutional neural networks
laryngoscopic image
Journal
The Laryngoscope
ISSN: 1531-4995
Titre abrégé: Laryngoscope
Pays: United States
ID NLM: 8607378
Informations de publication
Date de publication:
11 2020
11 2020
Historique:
received:
30
07
2019
revised:
17
12
2019
accepted:
30
12
2019
pubmed:
19
2
2020
medline:
1
1
2021
entrez:
19
2
2020
Statut:
ppublish
Résumé
To develop a deep-learning-based computer-aided diagnosis system for distinguishing laryngeal neoplasms (benign, precancerous lesions, and cancer) and improve the clinician-based accuracy of diagnostic assessments of laryngoscopy findings. Retrospective study. A total of 24,667 laryngoscopy images (normal, vocal nodule, polyps, leukoplakia and malignancy) were collected to develop and test a convolutional neural network (CNN)-based classifier. A comparison between the proposed CNN-based classifier and the clinical visual assessments (CVAs) by 12 otolaryngologists was conducted. In the independent testing dataset, an overall accuracy of 96.24% was achieved; for leukoplakia, benign, malignancy, normal, and vocal nodule, the sensitivity and specificity were 92.8% vs. 98.9%, 97% vs. 99.7%, 89% vs. 99.3%, 99.0% vs. 99.4%, and 97.2% vs. 99.1%, respectively. Furthermore, when compared with CVAs on the randomly selected test dataset, the CNN-based classifier outperformed physicians for most laryngeal conditions, with striking improvements in the ability to distinguish nodules (98% vs. 45%, P < .001), polyps (91% vs. 86%, P < .001), leukoplakia (91% vs. 65%, P < .001), and malignancy (90% vs. 54%, P < .001). The CNN-based classifier can provide a valuable reference for the diagnosis of laryngeal neoplasms during laryngoscopy, especially for distinguishing benign, precancerous, and cancer lesions. NA Laryngoscope, 130:E686-E693, 2020.
Types de publication
Evaluation Study
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
E686-E693Informations de copyright
© 2020 The American Laryngological, Rhinological and Otological Society, Inc.
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