I. Bayrakdar

Three-Dimensional Segmentation of Pulp Stones in Tooth-Level Volumes on Cone-Beam Computed Tomography Using nnU-Net v2: A Preliminary Study

Objectives: This study aimed to develop and evaluate an nnU-Net version 2 (nnU-Net v2)-based model for three-dimensional segmentation of pulp stones on cone-beam computed tomography (CBCT) images, with particular emphasis on segmentation performance within preselected tooth-level volumes of interest (VOIs) known to con...

Agreement between artificial intelligence and periodontists in the detection of periodontal bone loss on panoramic radiographs

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...

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...