K. Orhan

CRANIOSEG: Deep Learning-Based Automated Segmentation of Craniomaxillofacial Anatomical Structures on Cone-Beam Computed Tomography.

OBJECTIVE To develop and evaluate an nnU-Net v2-based deep-learning model for fully automated multiclass segmentation of 27 craniomaxillofacial anatomical structures on cone-beam computed tomography (CBCT). METHODS This retrospective study included CBCT scans from 106 adult patients. Twenty-seven anatomical structure...

Two-stage deep learning framework for magnetic resonance imaging-based detection of temporomandibular joint osteoarthritis.

Comparative Evaluation of MRI and Ultrasonography for Temporomandibular Joint Pathologies with Emphasis on Bone Marrow Edema.

Ulasonography appears suitable as a first-line screening modality for selected TMJ abnormalities, whereas magnetic resonance imaging remains indispensable whenever bone marrow pathology or complex internal derangement is suspected.

Evaluation of Differential Diagnosis of Odontogenic Lesions in Cone Beam Computed Tomography Images Using Radiomics-Based Machine Learning.

Panoramic radiography-based radiomic analysis demonstrates high diagnostic performance in non-invasively distinguishing mandibular bone alterations among T1DM, T2DM, and healthy individuals, with potential as a clinical bone monitoring tool.

Sex estimation from hand-wrist radiographic images: an artificial intelligence approach.

Both YOLOv8 and YOLOv11 outperformed YOLOv5 across all metrics, demonstrating superior feature extraction capacity and robust convergence and indicate that next-generation YOLO architectures enable highly accurate and fully automated sex estimation from hand-wrist radiographs without the need for manual annotation or r...