EP55 A fully automated approach to adult hip radiographs

Abstract

Abstract Purpose Plain radiographs are an essential, and often first line, tool employed in orthopedic practice for the evaluation of a wide range of hip pathologies and anatomic features. Their use ranges from the traumatic conditions such as fractures and dislocations to more chronic conditions such as hip dysplasia, and femoroacetabular impingement. Given this ubiquity, we propose a fully automated approach, or model, to studying these radiographs, employing convolutional neural networks (CNNs). We put forth that such an approach can produce measurements comparable with a trained individual and at a much faster rate. This can be useful in the clinical setting and in research where it enables the study of large datasets. Methods Our dataset includes hip radiographs from 82 patients. Anteroposterior (AP) and modified Dunn views are included and labelled by an orthopedic surgery resident highlighting anatomy of interest. A subset of this data is then used to train convolutional neural networks to perform the same task in a fully automated fashion. Custom developed code is then used to highlight landmarks of interest (such as the centre of the femoral head) and use these landmarks to calculate measurements used in evaluating adult hip anatomy. We tested the performance of this method against orthopedic surgery residents with respect to accuracy and speed. Results We compared the LCEA measurements performed by the automated model above to those of an orthopedic surgery resident as a first step. We show a mean difference of 5.75 degrees with a median of 3 degrees. Run time is approximately 800 milliseconds per radiograph on a consumer grade laptop. Conclusion Initial testing of the model developed suggests such an approach is feasible and can produce reliable results. Next steps are to include more data in model training to improve performance and then include more measurements on more views to achieve a complete evaluation of the adult hip. We then plan to use this model for studying large data sets and extract radiographic parameters with higher accuracy and reliability, and much higher speed than is done by human experts.

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