{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T02:12:56Z","timestamp":1788919976402,"version":"build-2803163510"},"reference-count":24,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T00:00:00Z","timestamp":1739232000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"Berta-Ottenstein-Programme for Clinician Scientists, Faculty of Medicine"},{"DOI":"10.13039\/501100002714","name":"University of Freiburg","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002714","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>This study evaluated the accuracy and reliability of deep learning-based segmentation techniques for mandibular canal identification in cone-beam CT (CBCT) data to provide a reliable and efficient support tool for dental implant treatment planning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A dataset of 90 CBCT scans was annotated as ground truth for mandibular canal segmentation. The dataset was split into training (n\u2009=\u200969), validation (n\u2009=\u20091), and testing (n\u2009=\u200920) subsets. A deep learning model based on a hierarchical convolutional neural network architecture was developed and trained. The model\u2019s performance was evaluated using dice similarity coefficient (DSC), 95% Hausdorff distance (HD), and average symmetric surface distance (ASSD). Qualitative assessment was performed by 2 experienced dental imaging practitioners who evaluated the segmentation quality in terms of trust and safety on a 5-point Likert scale at 3 mandibular locations per side.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The trained model achieved a mean DSC of 0.77\u2009\u00b1\u20090.09, HD of 1.66\u2009\u00b1\u20090.86\u2009mm, and ASSD of 0.31\u2009\u00b1\u20090.15\u2009mm on the testing subset. Qualitative assessment showed no significant difference between the deep learning-based segmentation and ground truth in terms of trust and safety across all investigated locations (P\u2009&amp;gt;\u20090.05).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed deep learning-based segmentation technique exhibits sufficient accuracy for the reliable identification of mandibular canals in CBCT scans. This automated approach could streamline the pre-operative planning process for dental implant placement, reducing the risk of neurovascular complications and enhancing patient safety.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/dmfr\/twae069","type":"journal-article","created":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T13:29:01Z","timestamp":1739280541000},"page":"279-285","source":"Crossref","is-referenced-by-count":8,"title":["Deep learning-based segmentation of the mandibular canals in cone-beam CT reaches human-level performance"],"prefix":"10.1093","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3255-3091","authenticated-orcid":false,"given":"Wiebke","family":"Semper-Hogg","sequence":"first","affiliation":[{"name":"Department of Oral and Maxillofacial Surgery, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg , 79106 Freiburg,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5881-6043","authenticated-orcid":false,"given":"Alexander","family":"Rau","sequence":"additional","affiliation":[{"name":"Department of Neuroradiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg , 79106 Freiburg,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marc Anton","family":"Fuessinger","sequence":"additional","affiliation":[{"name":"Department of Oral and Maxillofacial Surgery, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg , 79106 Freiburg,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sabrina","family":"Zimmermann","sequence":"additional","affiliation":[{"name":"Department of Oral and Maxillofacial Surgery, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg , 79106 Freiburg,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7460-3942","authenticated-orcid":false,"given":"Fabian","family":"Bamberg","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg , 79106 Freiburg,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marc Christian","family":"Metzger","sequence":"additional","affiliation":[{"name":"Department of Oral and Maxillofacial Surgery, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg , 79106 Freiburg,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rainer","family":"Schmelzeisen","sequence":"additional","affiliation":[{"name":"Department of Oral and Maxillofacial Surgery, University Medical Center Freiburg, Faculty of Medicine, University of 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