  Knee pain is undoubtedly the most common musculoskeletal symptom that impairs
quality of life, confines mobility and functionality across all ages. Knee pain
is clinically evaluated by routine radiographs, where the widespread adoption
of radiographic images and their availability at low cost, make them the
principle component in the assessment of knee pain and knee pathologies, such
as arthritis, trauma, and sport injuries. However, interpretation of the knee
radiographs is still highly subjective, and overlapping structures within the
radiographs and the large volume of images needing to be analyzed on a daily
basis, make interpretation challenging for both naive and experienced
practitioners. There is thus a need to implement an artificial intelligence
strategy to objectively and automatically interpret knee radiographs,
facilitating triage of abnormal radiographs in a timely fashion. The current
work proposes an accurate and effective pipeline for autonomous detection,
localization, and classification of knee joint area in plain radiographs
combining the You Only Look Once (YOLO v3) deep convolutional neural network
with a large and fully-annotated knee radiographs dataset. The present work is
expected to stimulate more interest from the deep learning computer vision
community to this pragmatic and clinical application.
