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

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...

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Bibliographic Details
Published in:arXiv.org 2022-02
Main Authors: Shi, Yan, Ramazanian, Taghi, Sagheb, Elham, Kremers, Walter K, Chaudhary, Vipin, Taunton, Michael, Kremers, Hilal Maradit, Tafti, Ahmad P
Format: Article
Language:English
Subjects:
Arthritis
Artificial intelligence
Artificial neural networks
Computer vision
Joints (anatomy)
Knee
Machine learning
Neural networks
Pain
Radiographs
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Summary: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.
ISSN:2331-8422