Oct 2026· Journal of imaging informatics in medicine· 0 citations· 15 references
Medicine
TL;DR
The proposed framework allows for the simultaneous and objective segmentation of mandibular odontogenic cysts and the mandibular canal within a unified, multi-class CBCT analysis framework, and could serve as a technical foundation for future quantitative analyses of lesion-canal relationships.
Convolutional neural network-based architectures have been applied to automatically delineate the edentulous alveolar ridge together with the mandibular canal and maxillary sinus, achieving accuracy that approaches manual segmentation performed by experienced clinicians while considerably shortening analysis time.
Rene Agatha, E. Astuti, Alhidayati Asymal· International Journal Of Sci...· 0 citations
Background/purpose Accurate automated segmentation of the maxillary sinus in cone-beam computed tomography (CBCT) scans is crucial for dental implant planning, sinus lift procedures, and pathological assessment, yet there remains a notable lack of publicly available models and datasets for this task. This study develop...
Hsuan Chen, K. D. Nguyen, Chih-Ling Chang et al.· Journal of Dental Sciences· 0 citations
This framework works best as a screening aid as it helps identify canals that need adjunctive three-dimensional evaluation and has higher sensitivity for severe curvature, compared to the limitations of two-dimensional radiographs in displaying buccolingual curvatures.
Balaganesh Pachaiappan, S. Poorni, Srividhya Srinivasan et al.· International Endodontic Jou...· 0 citations
Maxillary second molars with root fusion exhibit considerable anatomical variability that complicates dental treatment planning. Cone-beam computed tomography (CBCT) has become an essential imaging modality for evaluating such complex morphology. This study aimed to develop a deep learning (DL)-based computer-aided dia...
Tzu-Nin Weng, Chun-Hung Yang, Hsun-Yu Huang et al.· Journal of Functional Biomat...· 0 citations
This study demonstrates the technical feasibility of simultaneously segmenting 27 craniomaxillofacial anatomical structures from CBCT images using a single nnU-Net v2 model on a selected internal dataset and achieves strong segmentation performance.
I. Bayrakdar, Alican Kuran, Mehmet Uğurlu et al.· Dento maxillo facial radiolo...· 0 citations
The proposed approach provides objective OGE assessment from clinical intraoral photographs and may support future remote orthodontic monitoring after further validation with patient-acquired images.
Rui-Jie Zhang, Lang Lei, Xiang-Long Han et al.· E -journal of dentistry· 0 citations
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