{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T11:06:24Z","timestamp":1773140784470,"version":"3.50.1"},"reference-count":41,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2021,6,20]],"date-time":"2021-06-20T00:00:00Z","timestamp":1624147200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int Forum Allergy Rhinol"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Discrimination of nasal cavity mass lesions is a challenging work requiring extensive experience. A deep learning\u2010based automated diagnostic system may help clinicians to classify nasal cavity mass lesions. We demonstrated the feasibility of a convolutional neural network (CNN)\u2010based diagnosis system for automatic detection and classification of nasal polyps (NP) and inverted papillomas (IP).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We developed a CNN\u2010based 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\u2010based 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The algorithm achieved an overall accuracy of 0.742 \u00b1 0.058 with the following class accuracies: normal, 0.81\u00b1 0.14; IP, 0.57 \u00b1 0.07; and NP, 0.83 \u00b1 0.21. The AUC values for normal, IP, and NP were 0.91 \u00b1 0.06, 0.82 \u00b1 0.09, and 0.84 \u00b1 0.06, respectively. The overall accuracy of the CNN model was comparable with the average performance of human experts (0.742\u00a0vs. 0.749; <jats:italic>p\u00a0<\/jats:italic>=\u00a00.11).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec>","DOI":"10.1002\/alr.22854","type":"journal-article","created":{"date-parts":[[2021,6,20]],"date-time":"2021-06-20T15:37:26Z","timestamp":1624203446000},"page":"1637-1646","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Feasibility of a deep learning\u2010based algorithm for automated detection and classification of nasal polyps and inverted papillomas on nasal endoscopic images"],"prefix":"10.1002","volume":"11","author":[{"given":"Benton","family":"Girdler","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering University of Kentucky  Kentucky USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyun","family":"Moon","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology, Gangneung Asan Hospital University of Ulsan College of Medicine  Gangneung Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mi Rye","family":"Bae,","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology\u2010Head and Neck Surgery Bundang Jesaeng General Hospital  Seongnam Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sung Seok","family":"Ryu","sequence":"additional","affiliation":[{"name":"Department of Otorhinolaryngology\u2010Head and Neck Surgery, Asan Medical Center University of Ulsan College of Medicine  Seoul Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jihye","family":"Bae","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering University of Kentucky  Kentucky USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Myeong Sang","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Otorhinolaryngology\u2010Head and Neck Surgery, Asan Medical Center University of Ulsan College of Medicine  Seoul Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,6,20]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13193-016-0570-9"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.otc.2009.01.006"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.2147\/TCRM.S2379"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1093\/ije\/28.4.717"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/alr.21526"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.anorl.2016.03.006"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0022215110000599"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaci.2015.10.010"},{"key":"e_1_2_9_10_1","doi-asserted-by":"crossref","unstructured":"HameedN ShabutA HossainMA.A computer\u2010aided diagnosis system for classifying prominent skin lesions using machine learning.Paper presented at: 10th Computer Science and Electronic Engineering Conference (CEEC); September 19\u201021 2018;United Kingdom:Colchester.https:\/\/ieeexplore.ieee.org\/document\/8674183. 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