{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T11:57:36Z","timestamp":1791633456075,"version":"4.3.4"},"reference-count":33,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2018,8,11]],"date-time":"2018-08-11T00:00:00Z","timestamp":1533945600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100006108","name":"National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","award":["NCT01332136"],"award-info":[{"award-number":["NCT01332136"]}],"id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006108","name":"National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","award":["RO3 DC014809"],"award-info":[{"award-number":["RO3 DC014809"]}],"id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006108","name":"National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","award":["L30 AI113795"],"award-info":[{"award-number":["L30 AI113795"]}],"id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006108","name":"National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","award":["UL1TR000445"],"award-info":[{"award-number":["UL1TR000445"]}],"id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000055","name":"National Institute on Deafness and Other Communication Disorders","doi-asserted-by":"publisher","award":["R01 DC005805"],"award-info":[{"award-number":["R01 DC005805"]}],"id":[{"id":"10.13039\/100000055","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int Forum Allergy Rhinol"],"published-print":{"date-parts":[[2019,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Convolutional neural networks (CNNs) are advanced artificial intelligence algorithms well suited to image classification tasks with variable features. These have been used to great effect in various real\u2010world applications including handwriting recognition, face detection, image search, and fraud prevention. We sought to retrain a robust CNN with coronal computed tomography (CT) images to classify osteomeatal complex (OMC) occlusion and assess the performance of this technology with rhinologic data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The Google Inception\u2010V3 CNN trained with 1.28 million images was used as the base model. Preoperative coronal sections through the OMC were obtained from 239 patients enrolled in 2 prospective chronic rhinosinusitis (CRS) outcomes studies, labeled according to OMC status, and mirrored to obtain a set of 956 images. Using this data, the classification layer of Inception\u2010V3 was retrained in Python using a transfer learning method to adapt the CNN to the task of interpreting sinonasal CT images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The retrained neural network achieved 85% classification accuracy for OMC occlusion, with a 95% confidence interval for algorithm accuracy of 78% to 92%. Receiver operating characteristic (ROC) curve analysis on the test set confirmed good classification ability of the CNN with an area under the ROC curve (AUC) of 0.87, significantly different than both random guessing and a dominant classifier that predicts the most common class (\n                      <jats:italic>p<\/jats:italic>\n                      &lt; 0.0001).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Current state\u2010of\u2010the\u2010art CNNs may be able to learn clinically relevant information from 2\u2010dimensional sinonasal CT images with minimal supervision. Future work will extend this approach to 3\u2010dimensional images in order to further refine this technology for possible clinical applications.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/alr.22196","type":"journal-article","created":{"date-parts":[[2018,8,11]],"date-time":"2018-08-11T04:14:36Z","timestamp":1533960876000},"page":"46-52","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":76,"title":["Automated classification of osteomeatal complex inflammation on computed tomography using convolutional neural networks"],"prefix":"10.1002","volume":"9","author":[{"given":"Naweed I.","family":"Chowdhury","sequence":"first","affiliation":[{"name":"Vanderbilt University School of Medicine, Otolaryngology\u2013Head &amp; Neck Surgery  Nashville TN"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6424-7083","authenticated-orcid":false,"given":"Timothy L.","family":"Smith","sequence":"additional","affiliation":[{"name":"Oregon Health &amp; Science University, Department of Otolaryngology\u2013Head &amp; Neck Surgery  Portland OR"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rakesh K.","family":"Chandra","sequence":"additional","affiliation":[{"name":"Vanderbilt University School of Medicine, Otolaryngology\u2013Head &amp; Neck Surgery  Nashville TN"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Justin H.","family":"Turner","sequence":"additional","affiliation":[{"name":"Vanderbilt University School of Medicine, Otolaryngology\u2013Head &amp; Neck Surgery  Nashville TN"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2018,8,11]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/alr.21483"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/alr.21769"},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2014-006680"},{"key":"e_1_2_6_5_1","first-page":"183","article-title":"Staging in rhinosinusitus","volume":"31","author":"Lund VJ","year":"1993","journal-title":"Rhinology"},{"key":"e_1_2_6_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.anl.2013.04.010"},{"key":"e_1_2_6_7_1","doi-asserted-by":"publisher","DOI":"10.1177\/00034894041130S506"},{"key":"e_1_2_6_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/alr.21118"},{"key":"e_1_2_6_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.otohns.2007.02.004"},{"key":"e_1_2_6_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/alr.21552"},{"key":"e_1_2_6_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/alr.22014"},{"key":"e_1_2_6_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_2_6_13_1","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky A","year":"2012","journal-title":"Adv Neural Inf Process Syst"},{"key":"e_1_2_6_14_1","doi-asserted-by":"crossref","unstructured":"SzegedyC VanhouckeV IoffeS ShlensJ WojnaZ.Rethinking the inception architecture for computer vision. 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