  Hospital emergency departments frequently receive lots of bone fracture
cases, with pediatric wrist trauma fracture accounting for the majority of
them. Before pediatric surgeons perform surgery, they need to ask patients how
the fracture occurred and analyze the fracture situation by interpreting X-ray
images. The interpretation of X-ray images often requires a combination of
techniques from radiologists and surgeons, which requires time-consuming
specialized training. With the rise of deep learning in the field of computer
vision, network models applying for fracture detection has become an important
research topic. In this paper, we use data augmentation to improve the model
performance of YOLOv8 algorithm (the latest version of You Only Look Once) on a
pediatric wrist trauma X-ray dataset (GRAZPEDWRI-DX), which is a public
dataset. The experimental results show that our model has reached the
state-of-the-art (SOTA) mean average precision (mAP 50). Specifically, mAP 50
of our model is 0.638, which is significantly higher than the 0.634 and 0.636
of the improved YOLOv7 and original YOLOv8 models. To enable surgeons to use
our model for fracture detection on pediatric wrist trauma X-ray images, we
have designed the application "Fracture Detection Using YOLOv8 App" to assist
surgeons in diagnosing fractures, reducing the probability of error analysis,
and providing more useful information for surgery.
