Musculoskeletal disorders are a major healthcare challenge around the world. We investigate the utility of convolutional neural networks (CNNs) in performing generalized abnormality detection on lower extremity radiographs. We also explore the effect of pretraining, dataset size and model architecture on model performance to provide recommendations for future deep learning analyses on extremity radiographs, especially when access to large datasets is challenging. We collected a large dataset of 93,455 lower extremity radiographs of multiple body parts, with each exam labelled as normal or abnormal. A 161-layer densely connected, pretrained CNN achieved an AUC-ROC of 0.880 (sensitivity = 0.714, specificity = 0.961) on this abnormality classification task. Our findings show that a single CNN model can be effectively utilized for the identification of diverse abnormalities in highly variable radiographs of multiple body parts, a result that holds potential for improving patient triage and assisting with diagnostics in resource-limited settings.

肌肉骨骼疾病是世界范围内的主要医疗保健挑战。我们研究了卷积神经网络（CNN）在下肢X射线照片上执行广义异常检测的实用性。我们还探讨了预训练，数据集大小和模型体系结构对模型性能的影响，以为将来对四肢X射线照片进行深度学习分析提供建议，尤其是在访问大型数据集具有挑战性的情况下。我们收集了一个庞大的数据集，包含多个身体部位的93,455个下肢X光片，每次检查都标记为正常或异常。161层紧密连接的，经过预先训练的CNN在此异常分类任务上实现了0.880的AUC-ROC（灵敏度= 0.714，特异性= 0.961）。