背景:胸部X线摄影是检测肋骨骨折的标准方法。我们的研究旨在开发一种人工智能(AI)模型,只有相对少量的训练数据,可以在胸片上识别肋骨骨折并准确标记其精确位置,从而实现与医疗专业人员相当的诊断准确性。方法:对于这项回顾性研究,我们使用540张标记为Detectron2的胸部X线照片(270张正常照片和270张肋骨骨折照片)开发了一个AI模型,该模型结合了一个更快的基于区域的卷积神经网络(R-CNN),增强了特征金字塔网络(FPN).评估了模型对X线照片进行分类和检测肋骨骨折的能力。此外,我们将模型的性能与12名医生的性能进行了比较,包括6名经委员会认证的麻醉师和6名住院医师,通过观察者性能测试。结果:关于AI模型的射线照相分类性能,灵敏度,特异性,受试者工作特征曲线下面积(AUROC)分别为0.87、0.83和0.89。在肋骨断裂检测性能方面,灵敏度,假阳性率,自由反应接收器工作特性(JAFROC)品质因数(FOM)分别为0.62、0.3和0.76。AI模型在观察者绩效测试中与12名医生中的11名和12名医生中的10名相比没有统计学上的显着差异,分别。结论:我们开发了一个在有限的数据集上训练的AI模型,该模型显示了与经验丰富的医生相当的肋骨骨折分类和检测性能。
Background: Chest radiography is the standard method for detecting rib fractures. Our study aims to develop an artificial intelligence (AI) model that, with only a relatively small amount of training data, can identify rib fractures on chest radiographs and accurately mark their precise locations, thereby achieving a diagnostic accuracy comparable to that of medical professionals. Methods: For this retrospective study, we developed an AI model using 540 chest radiographs (270 normal and 270 with rib fractures) labeled for use with Detectron2 which incorporates a faster region-based convolutional neural network (R-CNN) enhanced with a feature pyramid network (FPN). The model\'s ability to classify radiographs and detect rib fractures was assessed. Furthermore, we compared the model\'s performance to that of 12 physicians, including six board-certified anesthesiologists and six residents, through an observer performance test. Results: Regarding the radiographic classification performance of the AI model, the sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) were 0.87, 0.83, and 0.89, respectively. In terms of rib fracture detection performance, the sensitivity, false-positive rate, and free-response receiver operating characteristic (JAFROC) figure of merit (FOM) were 0.62, 0.3, and 0.76, respectively. The AI model showed no statistically significant difference in the observer performance test compared to 11 of 12 and 10 of 12 physicians, respectively. Conclusions: We developed an AI model trained on a limited dataset that demonstrated a rib fracture classification and detection performance comparable to that of an experienced physician.