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Feasibility of a deep learning-based algorithm for automated detection and classification of nasal polyps and inverted papillomas on nasal endoscopic images

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Abstract
Background
Discrimination of nasal cavity mass lesions is a challenging work requiring extensive experience. A deep learning-based automated diagnostic system may help clinicians to classify nasal cavity mass lesions. We demonstrated the feasibility of a convolutional neural network (CNN)-based diagnosis system for automatic detection and classification of nasal polyps (NP) and inverted papillomas (IP).

Methods
We developed a CNN-based algorithm using a transfer learning strategy and trained it on nasal endoscopic images. A total of 99 nasal endoscopic images with normal findings, 98 images with NP, and 100 images with IP were analyzed using the developed CNN. Six otolaryngologists participated in clinical visual assessment. Image-based classification performance was measured by calculating the accuracy and area under the receiver operating characteristic curve (AUC). The diagnostic performance was compared between the CNN and clinical visual assessment by human experts.

Results
The algorithm achieved an overall accuracy of 0.742 ± 0.058 with the following class accuracies: normal, 0.81± 0.14; IP, 0.57 ± 0.07; and NP, 0.83 ± 0.21. The AUC values for normal, IP, and NP were 0.91 ± 0.06, 0.82 ± 0.09, and 0.84 ± 0.06, respectively. The overall accuracy of the CNN model was comparable with the average performance of human experts (0.742 vs. 0.749; p = 0.11).

Conclusions
The trained CNN model appears to reliably classify NP and IP of the nasal cavity from nasal endoscopic images; it also yields a reliable reference for diagnosing nasal cavity mass lesions during nasal endoscopy. However, further studies with more test data are warranted to improve the diagnostic accuracy of our CNN model.
Author(s)
류성석문현배미례유명상Benton Girdler BSJihye Bae
Issued Date
2021
Type
Article
Keyword
artificial intelligenceclinical visual assessmentconvolutional neural networkdeep learninginverted papillomanasal endoscopynasal polyp
DOI
10.1002/alr.22854
URI
https://oak.ulsan.ac.kr/handle/2021.oak/8513
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_proquest_miscellaneous_2543707557&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Feasibility%20of%20a%20deep%20learning-based%20algorithm%20for%20automated%20detection%20and%20classification%20of%20nasal%20polyps%20and%20inverted%20papillomas%20on%20nasal%20endoscopic%20images&offset=0&pcAvailability=true
Publisher
INTERNATIONAL FORUM OF ALLERGY & RHINOLOGY
Location
미국
Language
영어
ISSN
2042-6976
Citation Volume
11
Citation Number
12
Citation Start Page
1637
Citation End Page
1646
Appears in Collections:
Medicine > Medicine
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