{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T14:12:55Z","timestamp":1789999975281,"version":"4.0.1"},"reference-count":34,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T00:00:00Z","timestamp":1789776000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Temporomandibular disorders (TMDs) and sleep-related bruxism affect up to 40% and 31% of adults, respectively, and their management relies on accurate diagnostic assessment of the dental arch\u2014including tooth identification, arch-form analysis, and occlusal-plane evaluation\u2014followed by the fabrication of full-coverage hard occlusal stabilization splints. Current digital workflows still depend on manual CAD delineation for both diagnostic segmentation and splint design, introducing inter-operator variability. This study presents a computational framework that automates the diagnostic segmentation of 3D intraoral scans and the subsequent generation of patient-specific stabilization splints. Methods: Using the Teeth3DS+ dataset, 3D meshes are rendered into 32 views via a Fibonacci Hemisphere camera-placement strategy, and seven deep learning architectures are trained for pixel-level diagnostic classification of 34 dental structures. Predictions are back-projected onto the mesh through probability-aggregation voting and iterative graph diffusion; the occlusal plane is estimated by singular value decomposition of tooth centroids; and a volumetric dual-shell splint is synthesized by topological dilation and normal-based displacement. Results: UNet++ achieves the strongest performance (MPA 98.75%, mIoU 71.76%, Dice 75.20%), with its superiority over every competing architecture confirmed by both paired t-test and Wilcoxon signed-rank tests at the image and scan levels (all p &lt; 0.001). On 60 held-out scans, back-projection and graph diffusion raise segmentation quality from a per-view mIoU of 62.0% to a mesh-level mIoU of 87.4% (Dice from 65.2% to 91.3%), with every held-out scan improving. Per-class analysis reveals near-ceiling accuracy on well-represented teeth; performance on the rarest and most posterior teeth (third molars) was markedly lower in preliminary experiments but improved substantially once the learning rate was selected via a validation-based sweep\u2014a finding with direct implications for the diagnostic reliability of AI-assisted dental arch assessment and for the sensitivity of rare-class segmentation to training hyperparameters. Conclusions: The pipeline converts a raw intraoral scan into a diagnostically segmented model and a 3D-printable Michigan-type splint geometry without manual intervention, providing a foundation for accelerating the diagnostic-to-treatment cycle in TMD and bruxism management.<\/jats:p>","DOI":"10.3390\/diagnostics16183039","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T12:50:03Z","timestamp":1789995003000},"page":"3039","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated 3D Tooth Segmentation and Dental Splint Synthesis from Intraoral Scans: A Deep Learning Pipeline for Computer-Aided Digital Dentistry"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3602-2354","authenticated-orcid":false,"given":"Berrin","family":"\u00c7elik","sequence":"first","affiliation":[{"name":"Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara Y\u0131ld\u0131r\u0131m Beyaz\u0131t University, Ankara 06010, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5479-8876","authenticated-orcid":false,"given":"Mehmet","family":"\u00d6zkaya","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, Faculty of Engineering, Ankara University, Ankara 06830, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1766-5514","authenticated-orcid":false,"given":"Mahmut Emin","family":"\u00c7elik","sequence":"additional","affiliation":[{"name":"Electrical Electronics Engineering Department, Faculty of Engineering, Gazi University, Ankara 06570, T\u00fcrkiye"},{"name":"TUSA\u015e-Kazan Vocational School, Gazi University, Ankara 06980, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/j.tripleo.2011.04.021","article-title":"Research diagnostic criteria for temporomandibular disorders: A systematic review of axis I epidemiologic findings","volume":"112","author":"Manfredini","year":"2011","journal-title":"Oral Surg. 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