Objectives: The artificial intelligence-aided diagnosis model of rib fractures based on YOLOv3 algorithm was established and applied to practical case to explore the application advantages in rib fracture cases in forensic medicine.
Methods: DICOM format CT images of 884 cases with rib fractures caused by thoracic trauma were collected, and 801 of them were used as training and validation sets. A rib fracture diagnosis model based on YOLOv3 algorithm and Darknet53 as the backbone network was built. After the model was established, 83 cases were taken as the test set, and the precision rate, recall rate, F1-score and radiology interpretation time were calculated. The model was used to diagnose a practical case and compared with manual diagnosis.
Results: The established model was used to test 83 cases, the fracture precision rate of this model was 90.5%, the recall rate was 75.4%, F1-score was 0.82, the radiology interpretation time was 4.4 images per second and the identification time of each patient's data was 21 s, much faster than manual diagnosis. The recognition results of the model was consistent with that of the manual diagnosis.
Conclusions: The rib fracture diagnosis model in practical case based on YOLOv3 algorithm can quickly and accurately identify fractures, and the model is easy to operate. It can be used as an auxiliary diagnostic technique in forensic clinical identification.
目的: 建立基于YOLOv3算法的人工智能辅助肋骨骨折诊断模型并应用于实际案例，探讨该模型在法医临床常见肋骨骨折案例中的应用优势。方法: 收集884例胸部外伤致肋骨骨折患者的CT扫描DICOM格式图像，将其中801例作为训练集和验证集，搭建以YOLOv3算法为基础、Darknet53为骨干网络的肋骨骨折诊断模型，建模后以83例为测试集，计算精确率、召回率、F1分数、阅片时间。将该模型用于一起实际案例的诊断，并与人工诊断进行比较。结果: 使用建立的模型对83例进行测试，模型诊断骨折的精确率为90.5%，召回率为75.4%，F1分数为0.82，阅片时间为每秒4.4张，识别每位患者的数据花费时间平均为21 s，远快于人工阅片。所构建模型对实际案例的识别结果与人工诊断结果一致。结论: 基于YOLOv3算法的肋骨骨折诊断模型能够快速、准确地识别骨折，且操作简便，可在法医临床鉴定中作为辅助诊断技术。.
Keywords: Darknet53; YOLOv3; artificial intelligence (AI); computed tomography (CT); diagnosis; forensic medicine; rib fracture.