{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T17:31:38Z","timestamp":1788370298926,"version":"build-2803163510"},"reference-count":33,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T00:00:00Z","timestamp":1737072000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["Y22H185692"],"award-info":[{"award-number":["Y22H185692"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Am J Rhinol\ufffdAllergy"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Computed tomography (CT) plays a crucial role in assessing chronic rhinosinusitis, but lacks objective quantifiable indicators.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to use deep learning for automated sinus segmentation to generate distinct quantitative scores and explore their correlations with disease-specific quality of life.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>From July 2021 to August 2022, 445 CT data were collected from 2 medical centers. A deep learning model based on nnU-Net was trained for automatic sinus segmentation and internally validated using 300 cases. The remaining 145 cases were split into an external testing set (74 cases) and an independent testing set (71 cases). Two quantitative scores, the quantitative Lund-MacKay score and the quantitative opacification score (QOS), were derived from the segmentation results. The quantitative scores\u2019 efficacy was assessed by comparing them with the Lund-MacKay score (LMS), the 22-item Sinonasal Outcome Test score (SNOT-22), and other clinical variables through correlation analyses. Furthermore, the relationship between quantitative scores and postoperative quality of life improvement was explored using single-factor logistic regression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Result<\/jats:title>\n                    <jats:p>\n                      The segmentation model achieved average Dice similarity coefficients of 0.993, 0.978, 0.958, and 0.871 for the training, validation, external testing, and independent testing sets, respectively. Both quantitative scores significantly correlated with the LMS (\n                      <jats:italic>rho\u2009<\/jats:italic>\n                      =\u20090.87 and\n                      <jats:italic>rho\u2009<\/jats:italic>\n                      =\u20090.70,\n                      <jats:italic>P\u2009<\/jats:italic>\n                      &lt;\u2009.001). Neither score correlated with the total SNOT-22 score, although the modified QOS showed significant correlations with the nasal and sleep subdomains (\n                      <jats:italic>rho\u2009<\/jats:italic>\n                      =\u20090.26 and\n                      <jats:italic>rho\u2009<\/jats:italic>\n                      =\u20090.27,\n                      <jats:italic>P\u2009<\/jats:italic>\n                      &lt;\n                      <jats:italic>\u2009<\/jats:italic>\n                      .05). No significant association was found between quantitative score and postoperative improvement in quality of life.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Deep learning enables the automated segmentation of sinuses on CT scans, producing quantitative scores of sinus opacification. These automatic quantitative scores may serve as tools for chronic rhinosinusitis assessment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/19458924251313845","type":"journal-article","created":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T10:23:44Z","timestamp":1737109424000},"page":"187-196","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Deep Learning-Derived Quantitative Scores for Chronic Rhinosinusitis Assessment: Correlation With Quality of Life Outcomes"],"prefix":"10.1177","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-8763-5540","authenticated-orcid":false,"given":"Zhefan","family":"Shen","sequence":"first","affiliation":[{"name":"Department of Radiology, Affiliated Hospital of Jiaxing University, Jiaxing, P. R. 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China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuechun","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology, Hangzhou First People's Hospital, Hangzhou, P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongxiang","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Radiology, Hangzhou First People's Hospital, Hangzhou, P. R. 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