{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T20:00:54Z","timestamp":1785873654066,"version":"3.56.0"},"reference-count":42,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:00:00Z","timestamp":1701302400000},"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":["Clinical Oral Implants Res"],"published-print":{"date-parts":[[2024,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objectives<\/jats:title><jats:p>To investigate the accuracy and reliability of deep learning in automatic graft material segmentation after maxillary sinus augmentation (SA) from cone\u2010beam computed tomography (CBCT) images.<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials and Methods<\/jats:title><jats:p>One hundred paired CBCT scans (a preoperative scan and a postoperative scan) were collected and randomly allocated to training (<jats:italic>n<\/jats:italic>\u2009=\u200982) and testing (<jats:italic>n<\/jats:italic>\u2009=\u200918) subsets. The ground truths of graft materials were labeled by three observers together (two experienced surgeons and a computer engineer). A deep learning model including a 3D V\u2010Net and a 3D Attention V\u2010Net was developed. The overall performance of the model was assessed through the testing data set. The comparative accuracy and inference time consumption of the model\u2010driven and manual segmentation (by two surgeons with 3\u2009years of experience in dental implant surgery) were conducted on 10 CBCT scans from the test samples.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The deep learning model had a Dice coefficient (Dice) of 90.36\u2009\u00b1\u20092.53%, a 95% Hausdorff distance (HD) of 1.59\u2009\u00b1\u20090.82\u2009mm, and an average surface distance (ASD) of 0.38\u2009\u00b1\u20090.11\u2009mm. The proposed model only needed 7.2\u2009s, while the surgeon took 19.15\u2009min on average to complete a segmentation task. The overall performances of the model were significantly superior to those of surgeons.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The proposed deep learning model yielded a more accurate and efficient performance of automatic segmentation of graft material after SA than that of the two surgeons. The proposed model could facilitate a powerful system for volumetric change evaluation, dental implant planning, and digital dentistry.<\/jats:p><\/jats:sec>","DOI":"10.1111\/clr.14221","type":"journal-article","created":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T19:40:01Z","timestamp":1701373201000},"page":"964-972","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Deep learning\u2010based automatic segmentation of bone graft material after maxillary sinus augmentation"],"prefix":"10.1111","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0066-5470","authenticated-orcid":false,"given":"Baoxin","family":"Tao","sequence":"first","affiliation":[{"name":"Department of Second Dental Center Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine  Shanghai China"},{"name":"College of Stomatology Shanghai Jiao Tong University  Shanghai China"},{"name":"National Center for 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