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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...
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...
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