Department of Medicine, School of Clinical Sciences, Monash University, Victoria, Australia; Department of Endocrinology, Monash Health, Victoria, Australia; Department of Endocrinology and Diabetes, Western Health, Victoria, Australia; Department of Medicine, The University of Melbourne, Victoria, Australia.
Department of Medicine, School of Clinical Sciences, Monash University, Victoria, Australia; Department of Information Technology, Monash University, Victoria, Australia.
Bone. 2024 Oct;187:117215. doi: 10.1016/j.bone.2024.117215. Epub 2024 Jul 27.
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.
尽管影像学诊断非典型股骨骨折（AFF）有明确的标准，但漏诊和误诊仍很常见。AFF 诊断软件可以及时发现 AFF，防止不完全性骨折的进展或对侧 AFF 的发生。在这项研究中，我们研究了一种基于人工智能（AI）的应用程序，该程序使用深度学习模型（DLM），特别是卷积神经网络（CNN），从股骨 X 射线中检测 AFF 的能力。一个标记的澳大利亚数据集包括术前完全 AFF（cAFF）、不完全 AFF（iAFF）、典型股骨骨干骨折（TFF）和未骨折股骨（NFF）的前后位 X 射线图像，分别用于训练（N=213、49、394、1359）。使用预训练的（ImageNet 数据集）ResNet-50 骨干和新的 Box Attention Guide（BAG）模块开发了 AFFnet 模型，以引导模型的扫描模式，增强其学习能力。所有图像均用于通过 5 折交叉验证方法进行训练和内部测试，并使用外部数据集进行进一步验证。模型性能的外部验证是在一个瑞典数据集上进行的，该数据集包括 733 个 TFF 和 290 个 AFF 图像。在 AFFnet 和具有 ResNet-50 的全球方法之间测量并比较了精度、灵敏度、特异性、F1 评分和 AUC。两种模型均表现出出色的诊断性能（所有 AUC>0.97），但与 ResNet-50 相比，AFFnet 在内部和外部测试中记录的预测错误更少，且灵敏度、F1 评分和精度更高。AFFnet 检测 iAFF 的灵敏度高于 ResNet-50（82%比 56%）。总之，AFFnet 在内部和外部验证中均取得了出色的诊断性能，优于现有模型。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，从而改善患者的结局。
