{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T16:12:57Z","timestamp":1790266377667,"version":"4.1.0"},"reference-count":37,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T00:00:00Z","timestamp":1775865600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T00:00:00Z","timestamp":1775865600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100005089","name":"Natural Science Foundation of Beijing Municipality","doi-asserted-by":"publisher","award":["L222026"],"award-info":[{"award-number":["L222026"]}],"id":[{"id":"10.13039\/501100005089","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62506013"],"award-info":[{"award-number":["62506013"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int Endodontic J"],"abstract":"ABSTRACT<\/jats:title>\n \n Aim<\/jats:title>\n To explore a deep learning (DL) model for determining the relationship between the maxillary sinus (MS) and maxillary posterior teeth (MPT) based on cone beam computed tomography (CBCT) images and measuring the distance automatically between the MS and MPT using a 3D point cloud algorithm.<\/jats:p>\n <\/jats:sec>\n \n Methodology<\/jats:title>\n A CBCT dataset containing 88 maxillary sinuses (MSs) and 352 maxillary posterior teeth (MPT) was annotated, and the MS\u2010MPT distances were measured by clinicians as the ground truth. A segmentation model for MSs and MPT in CBCT images based on the U\u2010Net convolutional block attention (CBAM) architecture was trained and assessed using a 3\u2010fold cross\u2010validation strategy. Then, calibrated point clouds were reconstructed using segmented anatomical structure data, and the Euclidean distances between the MS and MPT were measured; the minimum distance was identified as the MS\u2010MPT distance. The performance of the model in terms of segmentation and distance measurement was evaluated, and the results were compared with the ground truth.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Our segmentation model achieved a mean Dice similarity coefficient (DSC) of 0.959 and a mean Jaccard coefficient of 0.922 for MSs and a mean DSC of 0.913 and a mean Jaccard coefficient of 0.851 for MPT. The MS\u2010MPT distances determined by clinicians and the 3D point cloud method demonstrated strong consistency (\n \u03d2<\/jats:italic>\n \u2009>\u20090.993,\n p<\/jats:italic>\n \u2009<\u20090.01). In terms of the model and clinicians, the mean negative signed error was 0.63\u2009mm (95% CI, 0.59\u20130.66\u2009mm), and the successful detection rate (SDR) for the root apex of MPT reached 70.3% at the 1\u2009mm threshold.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n In this study, an automated framework that combines deep learning\u2010driven segmentation and three\u2010dimensional point cloud analysis was developed to quantify the relationship between the maxillary sinus and maxillary posterior teeth and achieved reliable detection accuracy across diverse anatomical variations in CBCT scans.<\/jats:p>\n <\/jats:sec>","DOI":"10.1111\/iej.70141","type":"journal-article","created":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T07:05:59Z","timestamp":1775891159000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep\u2010Learning\u2010Based Automatic Measurement of the Distance Between the Maxillary Sinus and Maxillary Posterior Teeth on\n CBCT<\/scp>\n Images"],"prefix":"10.1111","author":[{"given":"Cheng\u2010Ye","family":"Li","sequence":"first","affiliation":[{"name":"Department of Cariology and Endodontology Peking University School and Hospital of Stomatology Beijing China"},{"name":"National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials Beijing China"},{"name":"Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & NHC Key Laboratory of Digital Stomatology & NMPA Key Laboratory for Dental Materials Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming\u2010Ming","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Cariology and Endodontology Peking University School and Hospital of Stomatology Beijing China"},{"name":"National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials Beijing China"},{"name":"Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & NHC Key Laboratory of Digital Stomatology & NMPA Key Laboratory for Dental Materials Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke\u2010Xin","family":"Yi","sequence":"additional","affiliation":[{"name":"School of Computer Science Peking University Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen\u2010Bing","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science Peking University Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Software and Microelectronics Peking University & National Engineering Research Center for Software Engineering, Peking University & Key Laboratory of High Confidence Software Technologies (Peking University), Ministry of Education Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kun","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Computer Science Peking University Beijing China"},{"name":"School of Software and Microelectronics Peking University & National Engineering Research Center for Software Engineering, Peking University & Key Laboratory of High Confidence Software Technologies (Peking University), Ministry of Education Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu\u2010Hong","family":"Liang","sequence":"additional","affiliation":[{"name":"National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials Beijing China"},{"name":"Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & NHC Key Laboratory of Digital Stomatology & NMPA Key Laboratory for Dental Materials Beijing China"},{"name":"Department of Oral Emergency Peking University School and Hospital of Stomatology Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,4,11]]},"reference":[{"key":"e_1_2_12_2_1","volume-title":"Head & Neck Surgery\u2014Otolaryngology","author":"Bailey B. 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