Affiliations: [a]
Center for International Education, Philippine Christian University, Manila, Philippines
| [b]
Department of Sports and Health, Linyi University, Shandong, Linyi, China
Correspondence:
[*]
Corresponding author: Guangyong Zhao, Department of Sports and Health, Linyi University, Shandong, Linyi, 276000 China. E-mail: [email protected].
Note: [1] This paper is published in the Special Issue of Medical Applications of X-Ray Imaging Techniques: Advances and Innovations. The Guest Editors are Dr. Malik Bader Alazzam (E-mail: [email protected]) and Dr. Amita Nandal (E-mail: [email protected]).
Abstract: BACKGROUND:In this research, imaging techniques such as CT and X-ray are used to locate important muscles in the shoulders and legs. Athletes who participate in sports that require running, jumping, or throwing are more likely to get injuries such as sprains, strains, tendinitis, fractures, and dislocations. One proposed automated technique has the overarching goal of enhancing recognition. OBJECTIVE:This study aims to determine how to recognize the major muscles in the shoulder and leg utilizing X-ray CT images as its primary diagnostic tool. METHODS:Using a shape model, discovering landmarks, and generating a form model are the steps necessary to identify injuries in key shoulder and leg muscles. The method also involves identifying injuries in significant abdominal muscles. The use of adversarial deep learning, and more specifically Deep-Injury Region Identification, can improve the ability to identify damaged muscle in X-ray and CT images. RESULTS:Applying the proposed diagnostic model to 150 sets of CT images, the study results show that Jaccard similarity coefficient (JSC) rate for the procedure is 0.724, the repeatability is 0.678, and the accuracy is 94.9% respectively. CONCLUSION:The study results demonstrate feasibility of using adversarial deep learning and deep-injury region identification to automatically detect severe muscle injuries in the shoulder and leg, which can enhance the identification and diagnosis of injuries in athletes, especially for those who compete in sports that include running, jumping, and throwing.
Keywords: Muscle injury, deep learning, generator-discriminator network, cross sectional area
