{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T07:48:09Z","timestamp":1768204089319,"version":"3.49.0"},"reference-count":57,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T00:00:00Z","timestamp":1767312000000},"content-version":"vor","delay-in-days":1,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100016808","name":"Natural Science Foundation of Xiamen Municipality","doi-asserted-by":"publisher","award":["3502Z20224ZD1323"],"award-info":[{"award-number":["3502Z20224ZD1323"]}],"id":[{"id":"10.13039\/100016808","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Concurrency and Computation"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>The prevalence of pulpal and periapical diseases exceeds 50% in the general population. Root canal treatment is currently recognized as the gold standard treatment, in which precise measurement of working length (WL) is critical for treatment success. In this study, a total of 100 eligible extracted teeth were collected and scanned using cone\u2010beam computed tomography (CBCT) to obtain high\u2010resolution three\u2010dimensional images. For WL calculation, we employed a V\u2010net\u2010based segmentation network for simulated paths of the root canal file, incorporating an encoder\u2013decoder structure and a multi\u2010task learning strategy, and achieved a sensitivity of 94.7%. Ablation studies revealed that integrating the decoder's mask branch, key point branch, and boundary branch significantly improved the segmentation accuracy. The WL calculation comprised three stages: skeleton extraction and noise suppression, branch extraction and generation of the simulated paths of the root canal file, and length calculation based on three\u2010dimensional spline curves. The model achieved an average prediction error of 0.28\u2009mm and an accuracy of 86.67% in WL prediction. These findings indicate that this V\u2010net\u2010based multi\u2010branch framework for precise WL estimation from CBCT holds substantial clinical application potential. Future work will focus on enhancing generalization and addressing challenges posed by calcified or anatomically complex root canals.<\/jats:p>","DOI":"10.1002\/cpe.70537","type":"journal-article","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T10:01:28Z","timestamp":1767348088000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Multi\u2010Task Learning V\u2010Net Model for Working Length Prediction in Volumetric Dental Cone Beam Computed Tomography Images"],"prefix":"10.1002","volume":"38","author":[{"given":"Jing","family":"Li","sequence":"first","affiliation":[{"name":"Stomatological Hospital of Xiamen Medical College &amp; Xiamen Key Laboratory of Stomatological Disease Diagnosis and Treatment  Xiamen China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Qiu","sequence":"additional","affiliation":[{"name":"Stomatological Hospital of Xiamen Medical College &amp; Xiamen Key Laboratory of Stomatological Disease Diagnosis and Treatment  Xiamen China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongcun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence Xiamen University  Xiamen China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9947-6687","authenticated-orcid":false,"given":"Huan","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Oral &amp; 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