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Image enhancement, denoising, and tissue segmentation improve data quality, enabling CNN-based injury identification and 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can single imaging diagnosis results for knee injuries be inaccurate?","Question",{"text":63,"@type":64},"Differences in doctors’ subjective experience and professional level, together with inconsistent diagnostic criteria, can reduce the accuracy and reliability of single-imaging diagnosis.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"What imaging modalities and learning methods are used in this study?",{"text":68,"@type":64},"The approach uses MRI, CT, and ultrasound (US) together with ensemble learning, and combines deep learning for automatic analysis.",{"name":70,"@type":61,"acceptedAnswer":71},"How is the deep learning model evaluated in the paper?",{"text":72,"@type":64},"Evaluation includes sensitivity and specificity for different injury types, time efficiency compared with manual interpretation, diagnostic consistency with clinicians, and generalization 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for Reprints to](1573-4056/25 Send Orders for Reprints to reprints@benthamscience.net)[ ](1573-4056/25 Send Orders for Reprints to reprints@benthamscience.net)[reprints@benthamscience.net](1573-4056/25 Send Orders for Reprints to reprints@benthamscience.net)  \n1  \nCurrent Medical Imaging  \nContent list available at: [https://benthamscience.com/journals/cmir](https://benthamscience.com/journals/cmir)  \nRESEARCH ARTICLE  \nAccuracy and Reliability of Multimodal Imaging in Diagnosing Knee Sports Injuries  \nDi Zhu1,£, Zitong Zhang2,* and Wenji Li1  \n1Sports College, Shandong Normal University, Jinan 250000, Shandong, China £Present Address: Sports College, Shinhan University, Gyeonggi-do 11626, South Korea 2Sports College, Shinhan University, Uijeongbu, Gyeonggi-do 11626, South Korea  \nAbstract:  \nBackground:  \nDue to differences in subjective experience and professional level among doctors, as well as inconsistent diagnostic criteria, there are issues with the accuracy and reliability of single imaging diagnosis results for knee joint injuries.  \nObjective:  \nTo address these issues, magnetic resonance imaging (MRI), computed tomography (CT) and ultrasound (US) are adopted in this article for ensemble learning, and deep learning (DL) is combined for automatic analysis.  \nMethods:  \nBy steps such as image enhancement, noise elimination, and tissue segmentation, the quality of image data is improved, and then convolutional neural networks (CNN) are used to automatically identify and classify injury types. The experimental results show that the DL model exhibits high sensitivity and specificity in the diagnosis of different types of injuries, such as anterior cruciate ligament tear, meniscus injury, cartilage injury, and fracture.  \nResults:  \nThe diagnostic accuracy of anterior cruciate ligament tear exceeds 90%, and the highest diagnostic accuracy of cartilage injury reaches 95.80% . In addition, compared with traditional manual image interpretation, the DL model has significant advantages in time efficiency, with a significant reduction in average interpretation time per case. The diagnostic consistency experiment shows that the DL model has high consistency with doctors’ diagnosis results, with an overall error rate of less than 2% .  \nConclusion:  \nThe model has high accuracy and strong generalization ability when dealing with different types of joint injuries. These data indicate that combining multiple imaging technologies and the DL algorithm can effectively improve the accuracy and efficiency of diagnosing sports injuries of knee joints.  \nKeywords: Medical image, Sports injuries of knee joints, Deep learning, Convolutional neural networks, Magnetic resonance imaging, Computed tomography, Ultrasound, Magnetic resonance imaging.  \n\n| Article History | Received: October 15, 2024 | Revised: February 20, 2025 | Accepted: March 06, 2025 |\n| --- | --- | --- | --- |\n\n1. INTRODUCTION  \nKnee joint injury is a common problem in sports medicine. In high-intensity sports, the incidence rate of knee joint injury  \n* Address correspondence to this author at the Sports College, Shinhan University, Uijeongbu, Gyeonggi-do 11626, South Korea;  \nE-mail: [salangheizzt@126.com](salangheizzt@126.com)  \nis high. The anatomical structure of the knee joint is complex, leading to more severe injuries. Precise diagnosis of knee joint injuries is the key to ensuring treatment effectiveness. At present, various imaging technologies have varying sensitivity and specificity for different types of knee joint injuries, and there are differences in the imaging diagnostic criteria for knee joint injuries among different medical institutions and doctors.  \nDOI: 10.2174/0115734056360665250506115221, 2025, 21, e15734056360665  \nOn this basis, the interpretation of medical images relies on the experience and professional level of doctors, which can easily lead to sub","cbCaiazJbnmysAde","https://ap.wps.com/l/cbCaiazJbnmysAde","pdf",15871453,13,"English","# Introduction\n# Materials and Methods\n## Image enhancement, denoising, and segmentation\n## CNN-based injury identification and classification\n# Results and Discussion\n## Sensitivity and specificity\n## Time efficiency and diagnostic consistency\n## Generalization ability\n# Conclusion\n# Keywords","[{\"question\":\"Why can single imaging diagnosis results for knee injuries be inaccurate?\",\"answer\":\"Differences in doctors’ subjective experience and professional level, together with inconsistent diagnostic criteria, can reduce the accuracy and reliability of single-imaging diagnosis.\"},{\"question\":\"What imaging modalities and learning methods are used in this study?\",\"answer\":\"The approach uses MRI, CT, and ultrasound (US) together with ensemble learning, and combines deep learning for automatic analysis.\"},{\"question\":\"How is the deep learning model evaluated in the paper?\",\"answer\":\"Evaluation includes sensitivity and specificity for different injury types, time efficiency compared with manual interpretation, diagnostic consistency with clinicians, and generalization performance.\"}]","Accuracy and Reliability of Multimodal Imaging in Diagnosing Knee Sports Injuries | PDF",1790744187,33]