Automated abnormality detection in lower extremity radiographs using deep learning

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. Identifying abnormalities in medical images across different viewing angles and body parts is a time-consuming task. Deep learning techniques hold great promise for supporting radiologists and improving patient triage decisions. A new study tests the viability of such approaches in resource-limited settings, exploring the effect of pretraining, dataset size and choice of deep learning model in the task of abnormality detection in lower-limb radiographs.