尽管非典型股骨骨折(AFF)的影像学诊断标准明确,漏诊和延误诊断很常见.AFF诊断软件可以提供及时的AFF检测以防止对侧AFF的不完全进展或发展。在这项研究中,我们研究了基于人工智能(AI)的应用程序的能力,使用深度学习模型(DLM),特别是卷积神经网络(CNN),从股骨X光片检测AFF。澳大利亚术前完整AFF(cAFF)的标记数据集,不完整的AFF(iAFF),典型股骨干骨折(TFF),和前后视图中的非骨折股骨(NFF)X射线图像用于训练(分别为N=213、49、394、1359)。AFFnet模型是使用预训练(ImageNet数据集)ResNet-50骨干开发的,和一个新颖的框注意指南(BAG)模块,以指导模型的扫描模式,以增强其学习。所有图像都用于训练和内部测试模型,使用5倍交叉验证方法,并通过外部数据集进一步验证。在包括733个TFF和290个AFF图像的瑞典数据集上进行模型性能的外部验证。Precision,灵敏度,特异性,在AFFnet和使用ResNet-50的全球方法之间测量并比较F1评分和AUC。在两个模型中都记录了出色的诊断性能(所有AUC>0.97),然而,AFFnet记录的预测误差较低,和改进的灵敏度,在内部和外部测试中与ResNet-50相比,F1评分和精度。AFFnet检测iAFF的灵敏度高于ResNet-50(82%vs56%)。总之,AFFnet在内部和外部验证中实现了出色的诊断性能,这优于预先存在的模型。准确的基于AI的AFF诊断软件有可能改善AFF诊断,减少放射科医生的错误,并允许紧急干预,从而改善患者的预后。
Despite well-defined criteria for radiographic diagnosis of atypical femur fractures (AFFs), missed and delayed diagnosis is common. An AFF diagnostic software could provide timely AFF detection to prevent progression of incomplete or development of contralateral AFFs. In this study, we investigated the ability for an artificial intelligence (AI)-based application, using deep learning models (DLMs), particularly convolutional neural networks (CNNs), to detect AFFs from femoral radiographs. A labelled Australian dataset of pre-operative complete AFF (cAFF), incomplete AFF (iAFF), typical femoral shaft fracture (TFF), and non-fractured femoral (NFF) X-ray images in anterior-posterior view were used for training (N = 213, 49, 394, 1359, respectively). An AFFnet model was developed using a pretrained (ImageNet dataset) ResNet-50 backbone, and a novel Box Attention Guide (BAG) module to guide the model\'s scanning patterns to enhance its learning. All images were used to train and internally test the model using a 5-fold cross validation approach, and further validated by an external dataset. External validation of the model\'s performance was conducted on a Sweden dataset comprising 733 TFF and 290 AFF images. Precision, sensitivity, specificity, F1-score and AUC were measured and compared between AFFnet and a global approach with ResNet-50. Excellent diagnostic performance was recorded in both models (all AUC >0.97), however AFFnet recorded lower number of prediction errors, and improved sensitivity, F1-score and precision compared to ResNet-50 in both internal and external testing. Sensitivity in the detection of iAFF was higher for AFFnet than ResNet-50 (82 % vs 56 %). In conclusion, AFFnet achieved excellent diagnostic performance on internal and external validation, which was superior to a pre-existing model. Accurate AI-based AFF diagnostic software has the potential to improve AFF diagnosis, reduce radiologist error, and allow urgent intervention, thus improving patient outcomes.