PURPOSE Demonstrate the feasibility of a fully automatic computer-aided diagnosis (CAD) tool, based on deep learning, that localizes and classifies proximal femur fractures on X-ray images according to the AO classification. The proposed framework aims to improve patient treatment planning and provide support for the training of trauma surgeon residents. MATERIAL AND METHODS A database of 1347 clinical radiographic studies was collected. Radiologists and trauma surgeons annotated all fractures with bounding boxes and provided a classification according to the AO standard. In all experiments, the dataset was split patient-wise in three with the ratio 70%:10%:20% to build the training, validation and test sets, respectively. ResNet-50 and AlexNet architectures were implemented as deep learning classification and localization models, respectively. Accuracy, precision, recall and [Formula: see text]-score were reported as classification metrics. Retrieval of similar cases was evaluated in terms of precision and recall. RESULTS The proposed CAD tool for the classification of radiographs into types "A," "B" and "not-fractured" reaches a [Formula: see text]-score of 87% and AUC of 0.95. When classifying fractures versus not-fractured cases it improves up to 94% and 0.98. Prior localization of the fracture results in an improvement with respect to full-image classification. In total, 100% of the predicted centers of the region of interest are contained in the manually provided bounding boxes. The system retrieves on average 9 relevant images (from the same class) out of 10 cases. CONCLUSION Our CAD scheme localizes, detects and further classifies proximal femur fractures achieving results comparable to expert-level and state-of-the-art performance. Our auxiliary localization model was highly accurate predicting the region of interest in the radiograph. We further investigated several strategies of verification for its adoption into the daily clinical routine. A sensitivity analysis of the size of the ROI and image retrieval as a clinical use case were presented.

中文翻译：

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精确的股骨近端骨折分类，用于交互式培训和手术计划。

目的演示基于深度学习的全自动计算机辅助诊断（CAD）工具的可行性，该工具可以根据AO分类在X射线图像上对股骨近端骨折进行定位和分类。拟议的框架旨在改善患者治疗计划，并为培训外科医生提供住所支持。材料与方法收集了1347项临床放射学研究的数据库。放射科医生和外科医生用边界框注释所有骨折，并根据AO标准进行分类。在所有实验中，按70％：10％：20％的比例将患者数据集按患者分为三部分，分别构建训练集，验证集和测试集。ResNet-50和AlexNet架构分别实现为深度学习分类和本地化模型。准确度，精确度，召回率和[公式：参见文字]得分均被报告为分类指标。根据准确性和召回率对类似案例的检索进行了评估。结果提出的用于将X线照片分类为“ A”，“ B”和“未断裂”类型的CAD工具的[公式：见文字]得分为87％，AUC为0.95。在对骨折和未骨折病例进行分类时，其改善率高达94％和0.98。骨折的先前定位导致全图像分类方面的改进。总计，感兴趣区域的预测中心的100％包含在手动提供的边界框中。系统平均从10个案例中检索9个相关图像（来自同一类别）。结论我们的CAD方案已本地化，检测并进一步对股骨近端骨折进行分类，其结果可与专家水平和最新技术相媲美。我们的辅助定位模型可以非常准确地预测射线照片中的感兴趣区域。我们进一步研究了将其纳入日常临床程序的几种验证策略。提出了对ROI大小和图像检索作为临床用例的敏感性分析。