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Knee pain is undoubtedly the most common musculoskeletal symptom that impairsquality of life, confines mobility and functionality across all ages. Knee painis clinically evaluated by routine radiographs, where the widespread adoptionof radiographic images and their availability at low cost, make them theprinciple component in the assessment of knee pain and knee pathologies, suchas arthritis, trauma, and sport injuries. However, interpretation of the kneeradiographs is still highly subjective, and overlapping structures within theradiographs and the large volume of images needing to be analyzed on a dailybasis, make interpretation challenging for both naive and experiencedpractitioners. There is thus a need to implement an artificial intelligencestrategy to objectively and automatically interpret knee radiographs,facilitating triage of abnormal radiographs in a timely fashion. The currentwork proposes an accurate and effective pipeline for autonomous detection,localization, and classification of knee joint area in plain radiographscombining the You Only Look Once (YOLO v3) deep convolutional neural networkwith a large and fully-annotated knee radiographs dataset. The present work isexpected to stimulate more interest from the deep learning computer visioncommunity to this pragmatic and clinical application.

Give me a knee radiograph, I will tell you where the knee joint area is: a deep convolutional neural network adventure