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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.
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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