The application of deep learning methods in knee joint sports injury diseases

IF 1.3 Q4 ENGINEERING, BIOMEDICAL

Computer Methods in Biomechanics and Biomedical Engineering-Imaging and Visualization Pub Date : 2023-09-23 DOI:10.1080/21681163.2023.2261554

Yeqiang Luo, Jing Liang, Shanghui Lin, Tianmo Bai, Lingchuang Kong, Yan Jin, Xin Zhang, Baofeng Li, Bei Chen

{"title":"The application of deep learning methods in knee joint sports injury diseases","authors":"Yeqiang Luo, Jing Liang, Shanghui Lin, Tianmo Bai, Lingchuang Kong, Yan Jin, Xin Zhang, Baofeng Li, Bei Chen","doi":"10.1080/21681163.2023.2261554","DOIUrl":null,"url":null,"abstract":"ABSTRACTDeep learning is a powerful branch of machine learning, which presents a promising new approach for diagnose diseases. However, the deep learning for detecting anterior cruciate ligament still limits to the evaluation of whether there are injuries. The accuracy of the deep learning model is not high, and the parameters are complex. In this study, we have developed a deep learning model based on ResNet-18 to detect ACL conditions. The results suggest that there is no significant difference between our proposed model and two orthopaedic surgeons and radiologists in diagnosing ACL conditions.KEYWORDS: Deep-learningmachine-learningautomated modelanterior cruciate ligament Disclosure statementThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.Data availability statementThis study used a MRNet dataset that gathered from Stanford University Medical Center. This dataset available online and anyone can be used.","PeriodicalId":51800,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering-Imaging and Visualization","volume":"14 1","pages":"0"},"PeriodicalIF":1.3000,"publicationDate":"2023-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Methods in Biomechanics and Biomedical Engineering-Imaging and Visualization","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/21681163.2023.2261554","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}

引用次数: 0

Abstract

ABSTRACTDeep learning is a powerful branch of machine learning, which presents a promising new approach for diagnose diseases. However, the deep learning for detecting anterior cruciate ligament still limits to the evaluation of whether there are injuries. The accuracy of the deep learning model is not high, and the parameters are complex. In this study, we have developed a deep learning model based on ResNet-18 to detect ACL conditions. The results suggest that there is no significant difference between our proposed model and two orthopaedic surgeons and radiologists in diagnosing ACL conditions.KEYWORDS: Deep-learningmachine-learningautomated modelanterior cruciate ligament Disclosure statementThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.Data availability statementThis study used a MRNet dataset that gathered from Stanford University Medical Center. This dataset available online and anyone can be used.

查看原文本刊更多论文

深度学习方法在膝关节运动损伤疾病中的应用

摘要深度学习是机器学习的一个强大分支，为疾病诊断提供了一种很有前景的新方法。然而，对于前交叉韧带检测的深度学习仍然局限于是否有损伤的评估。深度学习模型的精度不高，参数复杂。在本研究中，我们开发了一个基于ResNet-18的深度学习模型来检测ACL状况。结果表明，我们提出的模型与两位骨科医生和放射科医生在诊断ACL状况方面没有显著差异。关键词:深度学习机器学习自动模型前交叉韧带披露声明作者声明本研究是在没有任何商业或财务关系的情况下进行的，这可能被解释为潜在的利益冲突。数据可用性声明本研究使用了从斯坦福大学医学中心收集的MRNet数据集。此数据集可在线获取，任何人都可以使用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Computer Methods in Biomechanics and Biomedical Engineering-Imaging and Visualization ENGINEERING, BIOMEDICAL-

CiteScore

2.80

自引率

6.20%

发文量

102

期刊介绍： Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization is an international journal whose main goals are to promote solutions of excellence for both imaging and visualization of biomedical data, and establish links among researchers, clinicians, the medical technology sector and end-users. The journal provides a comprehensive forum for discussion of the current state-of-the-art in the scientific fields related to imaging and visualization, including, but not limited to: Applications of Imaging and Visualization Computational Bio- imaging and Visualization Computer Aided Diagnosis, Surgery, Therapy and Treatment Data Processing and Analysis Devices for Imaging and Visualization Grid and High Performance Computing for Imaging and Visualization Human Perception in Imaging and Visualization Image Processing and Analysis Image-based Geometric Modelling Imaging and Visualization in Biomechanics Imaging and Visualization in Biomedical Engineering Medical Clinics Medical Imaging and Visualization Multi-modal Imaging and Visualization Multiscale Imaging and Visualization Scientific Visualization Software Development for Imaging and Visualization Telemedicine Systems and Applications Virtual Reality Visual Data Mining and Knowledge Discovery.