{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T22:34:46Z","timestamp":1792103686563,"version":"4.4.0"},"reference-count":37,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T00:00:00Z","timestamp":1592179200000},"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":[[2020,11]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Computed tomography (CT) plays a key role in evaluation of paranasal sinus inflammation, but improved, and standardized, objective assessment is needed. Computerized volumetric analysis has benefits over visual scoring, but typically relies on manual image segmentation, which is difficult and time\u2010consuming, limiting practical applicability. We hypothesized that a convolutional neural network (CNN) algorithm could perform automatic, volumetric segmentation of the paranasal sinuses on CT, enabling efficient, objective measurement of sinus opacification. In this study we performed initial clinical testing of a CNN for fully automatic quantitation of paranasal sinus opacification in the diagnostic workup of patients with chronic upper and lower airway disease.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Sinus CT scans were collected on 690 patients who underwent imaging as part of multidisciplinary clinical workup at a tertiary care respiratory hospital between April 2016 and November 2017. A CNN was trained to perform automatic segmentation using a subset of CTs (n = 180) that were segmented manually. A nonoverlapping set (n = 510) was used for testing. CNN opacification scores were compared with Lund\u2010MacKay (LM) visual scores, pulmonary function test results, and other clinical variables using Spearman correlation and linear regression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      CNN scores were correlated with LM scores (rho = 0.82,\n                      <jats:italic>p<\/jats:italic>\n                      &lt; 0.001) and with forced expiratory volume in 1 second (FEV\n                      <jats:sub>1<\/jats:sub>\n                      ) percent predicted (rho = \u22120.21,\n                      <jats:italic>p<\/jats:italic>\n                      &lt; 0.001), FEV\n                      <jats:sub>1<\/jats:sub>\n                      \/forced vital capacity ratio (rho = \u22120.27,\n                      <jats:italic>p<\/jats:italic>\n                      &lt; 0.001), immunoglobulin E (rho = 0.20,\n                      <jats:italic>p<\/jats:italic>\n                      &lt; 0.001), eosinophil count (rho = 0.28,\n                      <jats:italic>p<\/jats:italic>\n                      &lt; 0.001), and exhaled nitric oxide (rho = 0.40,\n                      <jats:italic>p<\/jats:italic>\n                      &lt; 0.001).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Segmentation of the paranasal sinuses on CT can be automated using a CNN, providing truly objective, volumetric quantitation of sinonasal inflammation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/alr.22588","type":"journal-article","created":{"date-parts":[[2020,4,19]],"date-time":"2020-04-19T13:54:55Z","timestamp":1587304495000},"page":"1218-1225","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["Volumetric assessment of paranasal sinus opacification on computed tomography can be automated using a convolutional neural network"],"prefix":"10.1002","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5113-4530","authenticated-orcid":false,"given":"Stephen M.","family":"Humphries","sequence":"first","affiliation":[{"name":"Department of Radiology National Jewish Health  Denver CO"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan Pablo","family":"Centeno","sequence":"additional","affiliation":[{"name":"Department of Radiology National Jewish Health  Denver CO"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aleena M.","family":"Notary","sequence":"additional","affiliation":[{"name":"Department of Radiology National Jewish Health  Denver CO"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Justin","family":"Gerow","sequence":"additional","affiliation":[{"name":"Department of Radiology National Jewish Health  Denver CO"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giuseppe","family":"Cicchetti","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Imaging Radiation Oncology and Hematology Fondazione Policlinico Universitario \u201cA. Gemelli\u201d IRCCS  Rome Italy"},{"name":"Radiology Institute Universit\u00e0 Cattolica del Sacro Cuore  Rome Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rohit K.","family":"Katial","sequence":"additional","affiliation":[{"name":"Division of Allergy &amp; Clinical Immunology National Jewish Health  Denver CO"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8612-5442","authenticated-orcid":false,"given":"Daniel M.","family":"Beswick","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology\u2012Head and Neck Surgery University of Colorado School of Medicine  Aurora CO"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2748-0705","authenticated-orcid":false,"given":"Vijay R.","family":"Ramakrishnan","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology\u2012Head and Neck Surgery University of Colorado School of Medicine  Aurora CO"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rafeul","family":"Alam","sequence":"additional","affiliation":[{"name":"Division of Allergy &amp; Clinical Immunology National Jewish Health  Denver CO"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David A.","family":"Lynch","sequence":"additional","affiliation":[{"name":"Department of Radiology National Jewish Health  Denver CO"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2020,6,15]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/alr.21219"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1177\/0194599813513881"},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/alr.22014"},{"key":"e_1_2_6_5_1","first-page":"S1","article-title":"Clinical practice guideline (update): adult sinusitis","volume":"152","author":"Rosenfeld RM","year":"2015","journal-title":"Otolaryngol Neck Surg"},{"key":"e_1_2_6_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/alr.21671"},{"key":"e_1_2_6_7_1","first-page":"183","article-title":"Staging in rhinosinusitis","volume":"31","author":"Lund VJ","year":"1993","journal-title":"Rhinology"},{"key":"e_1_2_6_8_1","doi-asserted-by":"publisher","DOI":"10.1067\/mai.2002.122458"},{"key":"e_1_2_6_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/alr.22109"},{"key":"e_1_2_6_10_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2015.19330"},{"key":"e_1_2_6_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.otohns.2007.02.004"},{"key":"e_1_2_6_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2213-2600(18)30286-8"},{"key":"e_1_2_6_13_1","doi-asserted-by":"publisher","DOI":"10.1164\/rccm.201705-0860OC"},{"key":"e_1_2_6_14_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2019191022"},{"key":"e_1_2_6_15_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2017161177"},{"key":"e_1_2_6_16_1","doi-asserted-by":"publisher","DOI":"10.1183\/13993003.01384-2018"},{"key":"e_1_2_6_17_1","doi-asserted-by":"crossref","unstructured":"RonnebergerO FischerP BroxT.U\u2010net: convolutional networks for biomedical image segmentation. 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