{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T03:41:32Z","timestamp":1792381292817,"version":"4.4.0"},"reference-count":45,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T00:00:00Z","timestamp":1738540800000},"content-version":"vor","delay-in-days":2,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clin Implant Dent Rel Res"],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Maxillary sinus mucosal cysts represent prevalent oral and maxillofacial diseases, and their precise diagnosis is essential for surgical planning in maxillary sinus floor elevation. This study aimed to develop a deep learning\u2010based pipeline for the classification of maxillary sinus lesions in cone beam computed tomography (CBCT) images to provide auxiliary support for clinical diagnosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study utilized 45\u2009136 maxillary sinus images from CBCT scans of 541 patients. A cascade network was designed, comprising a semi\u2010supervised maxillary sinus area object detection module and a maxillary sinus lesions classification module. The object detection module employed a semi\u2010supervised pseudo\u2010labelling training strategy to expand the maxillary sinus annotation dataset. In the classification module, the performance of Convolutional Neural Network and Transformer architectures was compared for maxillary sinus mucosal lesion classification. The object detection and classification modules were evaluated using metrics including Accuracy, Precision, Recall, F1 score, and Average Precision, with the object detection module additionally assessed using Precision\u2010Recall Curve.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The fully supervised pseudo\u2010label generation model achieved an average accuracy of 0.9433, while the semi\u2010supervised maxillary sinus detection model attained 0.9403. ResNet\u201050 outperformed in classification, with accuracies of 0.9836 (sagittal) and 0.9797 (coronal). Grad\u2010CAM visualization confirmed accurate focus on clinically relevant lesion features.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed pipeline achieves high\u2010precision detection and classification of maxillary sinus mucosal lesions, reducing manual annotation while maintaining accuracy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/cid.13431","type":"journal-article","created":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T08:59:41Z","timestamp":1738573181000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Accuracy of a Cascade Network for Semi\u2010Supervised Maxillary Sinus Detection and Sinus Cyst Classification"],"prefix":"10.1111","volume":"27","author":[{"given":"Xueqi","family":"Guo","sequence":"first","affiliation":[{"name":"Department of Oral Implantology, School and Hospital of Stomatology, Guangdong Engineering Research Center of Oral Restoration and Reconstruction &amp; Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine Guangzhou Medical University  Guangzhou Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8473-5652","authenticated-orcid":false,"given":"Zelun","family":"Huang","sequence":"additional","affiliation":[{"name":"The Affiliated Guangdong Second Provincial General Hospital of Jinan University  Guangzhou Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jieying","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Oral Implantology, School and Hospital of Stomatology, Guangdong Engineering Research Center of Oral Restoration and Reconstruction &amp; Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine Guangzhou Medical University  Guangzhou Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jialing","family":"Wei","sequence":"additional","affiliation":[{"name":"Shenzhen Technology University  Shenzhen Guangdong Province China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongshan","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Oral Implantology, School and Hospital of Stomatology, Guangdong Engineering Research Center of Oral Restoration and Reconstruction &amp; Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine Guangzhou Medical University  Guangzhou Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoran","family":"Zheng","sequence":"additional","affiliation":[{"name":"Department of Chemical &amp; Materials Engineering University of Auckland  Auckland New 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