  Wrist fractures are highly prevalent among children and can significantly
impact their daily activities, such as attending school, participating in
sports, and performing basic self-care tasks. If not treated properly, these
fractures can result in chronic pain, reduced wrist functionality, and other
long-term complications. Recently, advancements in object detection have shown
promise in enhancing fracture detection, with systems achieving accuracy
comparable to, or even surpassing, that of human radiologists. The YOLO series,
in particular, has demonstrated notable success in this domain. This study is
the first to provide a thorough evaluation of various YOLOv10 variants to
assess their performance in detecting pediatric wrist fractures using the
GRAZPEDWRI-DX dataset. It investigates how changes in model complexity, scaling
the architecture, and implementing a dual-label assignment strategy can enhance
detection performance. Experimental results indicate that our trained model
achieved mean average precision (mAP@50-95) of 51.9\% surpassing the current
YOLOv9 benchmark of 43.3\% on this dataset. This represents an improvement of
8.6\%. The implementation code is publicly available at
https://github.com/ammarlodhi255/YOLOv10-Fracture-Detection
