Detection of ankle fractures using deep learning algorithms

Background: Early and accurate detection of ankle fractures are crucial for optimizing treatment and thus reducing future complications. Radiographs are the most abundant imaging techniques for assessing fractures. Deep learning (DL) methods, through adequately trained deep convolutional neural networks (DCNNs), have been previously shown to faster and accurately analyze radiographic images without human intervention. Herein, we aimed to assess the performance of two different DCNNs in detecting ankle fractures using radiographs compared to the ground truth. Methods: In this retrospective case-control study, our DCNNs were trained using radiographs obtained from 1050 patients with ankle fracture and the same number of individuals with otherwise healthy ankles. Inception V3 and Renet-50 pretrained models were used in our algorithms. Danis-Weber classification method was used. Out of 1050, 72 individuals were labeled as occult fractures as they were not detected in the primary radiographic assessment. Single-view (anteroposterior) radiographs was compared with 3-views (anteroposterior, mortise, lateral) for training the DCNNs. Results: Our DCNNs showed a better performance using 3-views images versus single-view based on greater values for accuracy, F-score, and area under the curve (AUC). The highest sensitivity was 98.7 % and specificity was 98.6 % in detection of ankle fractures using 3-views using inception V3. This model missed only one fracture on radiographs. Conclusion: The performance of our DCNNs showed that it can be used for developing the currently used image interpretation programs or as a separate assistant solution for the clinicians to detect ankle fractures faster and more precisely. Level of evidence: III