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, YOLOv8 algorithm is used to train models on the GRAZPEDWRI-DX dataset, which includes X-ray images from 6,091 pediatric patients with wrist trauma. The experimental results show that YOLOv8 algorithm models have different advantages for different model sizes, with YOLOv8l model achieving the highest mean average precision (mAP 50) of 63.6\%, and YOLOv8n model achieving the inference time of 67.4ms per X-ray image on one single CPU with low computing power. In this way, we create "Fracture Detection Using YOLOv8 App" to assist surgeons in interpreting X-ray images without the help of radiologists. Our implementation code is released at https://github.com/RuiyangJu/Bone_Fracture_Detection_YOLOv8.

翻译：医院急诊科经常接收大量骨折病例，其中小儿腕部创伤骨折占大多数。在小儿外科医生进行手术前，需要询问患者骨折发生情况，并通过解读X光片分析骨折状况。X光片的解读通常需要放射科医生和外科医生的技术结合，这需要耗费时间的专业培训。随着深度学习在计算机视觉领域的兴起，应用于骨折检测的网络模型已成为重要研究课题。本文采用YOLOv8算法在GRAZPEDWRI-DX数据集上训练模型，该数据集包含来自6,091名腕部创伤儿科患者的X光片。实验结果表明，不同规模的YOLOv8算法模型各具优势，其中YOLOv8l模型获得了最高平均精度（mAP 50）达63.6%，而YOLOv8n模型在低算力的单CPU上实现了每张X光片67.4毫秒的推理时间。基于此，我们创建了"基于YOLOv8的骨折检测应用"，帮助外科医生在无需放射科医生协助的情况下解读X光片。我们的实现代码已在https://github.com/RuiyangJu/Bone_Fracture_Detection_YOLOv8上开源。