{"title":"使用预先训练的 DenseNet201 结合 Capsule 网络对肩关节植入物制造商进行分类。","authors":"Xianzhong Jian, Zhenling Zhou, Wuwen Zhang","doi":"10.1002/rcs.2672","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>This study aims to accelerate revision surgery and treatment using X-ray imaging and deep learning to identify shoulder implant manufacturers in advance.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>A feature engineering approach based on principal component analysis and a k-means algorithm was used to cluster shoulder implant data. In addition, a pre-trained DenseNet201 combined with a capsule network (DenseNet201-Caps) shoulder implant classification model was proposed.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>DenseNet201-Caps was the most effective classification model on the clustered dataset with an accuracy of 94.25% and an <i>F</i>1 score of 96.30%. Notably, clustering the dataset in advance improved the accuracy and the Caps implementations successfully enhanced the performance of all convolutional neural network models. The analysed results indicate that DenseNet201-Caps struggled to distinguish between the Cofield and Depuy manufacturers. Hence, a multistage classification approach was developed with an improved accuracy of 96.55% achieved.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>The DenseNet201-Caps method enables the accurate identification of shoulder implant manufacturers.</p>\n </section>\n </div>","PeriodicalId":50311,"journal":{"name":"International Journal of Medical Robotics and Computer Assisted Surgery","volume":"20 5","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Classification of Shoulder Implant Manufacturer Using Pre-Trained DenseNet201 Combined With Capsule Network\",\"authors\":\"Xianzhong Jian, Zhenling Zhou, Wuwen Zhang\",\"doi\":\"10.1002/rcs.2672\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>This study aims to accelerate revision surgery and treatment using X-ray imaging and deep learning to identify shoulder implant manufacturers in advance.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>A feature engineering approach based on principal component analysis and a k-means algorithm was used to cluster shoulder implant data. In addition, a pre-trained DenseNet201 combined with a capsule network (DenseNet201-Caps) shoulder implant classification model was proposed.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>DenseNet201-Caps was the most effective classification model on the clustered dataset with an accuracy of 94.25% and an <i>F</i>1 score of 96.30%. Notably, clustering the dataset in advance improved the accuracy and the Caps implementations successfully enhanced the performance of all convolutional neural network models. The analysed results indicate that DenseNet201-Caps struggled to distinguish between the Cofield and Depuy manufacturers. Hence, a multistage classification approach was developed with an improved accuracy of 96.55% achieved.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>The DenseNet201-Caps method enables the accurate identification of shoulder implant manufacturers.</p>\\n </section>\\n </div>\",\"PeriodicalId\":50311,\"journal\":{\"name\":\"International Journal of Medical Robotics and Computer Assisted Surgery\",\"volume\":\"20 5\",\"pages\":\"\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-09-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Medical Robotics and Computer Assisted Surgery\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/rcs.2672\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"SURGERY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Medical Robotics and Computer Assisted Surgery","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/rcs.2672","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"SURGERY","Score":null,"Total":0}
Classification of Shoulder Implant Manufacturer Using Pre-Trained DenseNet201 Combined With Capsule Network
Background
This study aims to accelerate revision surgery and treatment using X-ray imaging and deep learning to identify shoulder implant manufacturers in advance.
Methods
A feature engineering approach based on principal component analysis and a k-means algorithm was used to cluster shoulder implant data. In addition, a pre-trained DenseNet201 combined with a capsule network (DenseNet201-Caps) shoulder implant classification model was proposed.
Results
DenseNet201-Caps was the most effective classification model on the clustered dataset with an accuracy of 94.25% and an F1 score of 96.30%. Notably, clustering the dataset in advance improved the accuracy and the Caps implementations successfully enhanced the performance of all convolutional neural network models. The analysed results indicate that DenseNet201-Caps struggled to distinguish between the Cofield and Depuy manufacturers. Hence, a multistage classification approach was developed with an improved accuracy of 96.55% achieved.
Conclusions
The DenseNet201-Caps method enables the accurate identification of shoulder implant manufacturers.
期刊介绍：
The International Journal of Medical Robotics and Computer Assisted Surgery provides a cross-disciplinary platform for presenting the latest developments in robotics and computer assisted technologies for medical applications. The journal publishes cutting-edge papers and expert reviews, complemented by commentaries, correspondence and conference highlights that stimulate discussion and exchange of ideas. Areas of interest include robotic surgery aids and systems, operative planning tools, medical imaging and visualisation, simulation and navigation, virtual reality, intuitive command and control systems, haptics and sensor technologies. In addition to research and surgical planning studies, the journal welcomes papers detailing clinical trials and applications of computer-assisted workflows and robotic systems in neurosurgery, urology, paediatric, orthopaedic, craniofacial, cardiovascular, thoraco-abdominal, musculoskeletal and visceral surgery. Articles providing critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies, commenting on ease of use, or addressing surgical education and training issues are also encouraged. The journal aims to foster a community that encompasses medical practitioners, researchers, and engineers and computer scientists developing robotic systems and computational tools in academic and commercial environments, with the intention of promoting and developing these exciting areas of medical technology.
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