{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T08:33:07Z","timestamp":1790411587814,"version":"4.1.0"},"reference-count":21,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100004835","name":"zhejiang university","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004835","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Dent Res"],"published-print":{"date-parts":[[2022,3]]},"abstract":"<jats:p>Digital dentistry plays a pivotal role in dental health care. A critical step in many digital dental systems is to accurately delineate individual teeth and the gingiva in the 3-dimension intraoral scanned mesh data. However, previous state-of-the-art methods are either time-consuming or error prone, hence hindering their clinical applicability. This article presents an accurate, efficient, and fully automated deep learning model trained on a data set of 4,000 intraoral scanned data annotated by experienced human experts. On a holdout data set of 200 scans, our model achieves a per-face accuracy, average-area accuracy, and area under the receiver operating characteristic curve of 96.94%, 98.26%, and 0.9991, respectively, significantly outperforming the state-of-the-art baselines. In addition, our model takes only about 24\u2009s to generate segmentation outputs, as opposed to &gt;5\u2009min by the baseline and 15\u2009min by human experts. A clinical performance test of 500 patients with malocclusion and\/or abnormal teeth shows that 96.9% of the segmentations are satisfactory for clinical applications, 2.9% automatically trigger alarms for human improvement, and only 0.2% of them need rework. Our research demonstrates the potential for deep learning to improve the efficacy and efficiency of dental treatment and digital dentistry.<\/jats:p>","DOI":"10.1177\/00220345211040459","type":"journal-article","created":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T08:20:26Z","timestamp":1635754826000},"page":"304-311","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":66,"title":["Toward Clinically Applicable 3-Dimensional Tooth Segmentation via Deep Learning"],"prefix":"10.1177","volume":"101","author":[{"given":"J.","family":"Hao","sequence":"first","affiliation":[{"name":"State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases and West China Hospital of Stomatology, Sichuan University, Chengdu, China"},{"name":"Harvard School of Dental Medicine, Harvard University, Boston, MA, 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