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:医院急诊部经常接收许多骨折病例,儿童手腕外伤骨折占其中的大多数。在小儿外科医生进行手术之前,他们需要询问病人骨折发生的情况,并通过解读X光图像来分析骨折情况。解读X光图像通常需要放射科医师和外科医师的技术结合,需要耗费时间和专业培训。随着深度学习在计算机视觉领域中的兴起,应用于骨折检测的网络模型已成为重要的研究课题。本文使用YOLOv8算法对GRAZPEDWRI-DX数据集进行模型训练,该数据集包含来自6,091名手腕外伤儿童患者的X光图像。实验结果表明,YOLOv8算法模型对于不同的模型大小具有不同的优势,其中YOLOv8l模型的平均精度最高(mAP 50为63.6%),而YOLOv8n模型在具有低计算能力的单个CPU上的推理时间为67.4ms每个X光图像。通过这种方式,我们创建了“使用YOLOv8应用程序检测骨折”来协助外科医生在没有放射科医师帮助下解读X光图像。我们的实现代码发布在https://github.com/RuiyangJu/Bone_Fracture_Detection_YOLOv8 上。