[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121966-en":3,"doc-seo-121966-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121966,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Prediction of knee biomechanics with different tibial component malrotations after total knee arthroplasty - conventional machine learning vs. deep learning","Accurate tibiofemoral alignment in total knee arthroplasty is essential for durability and function, yet rapid prediction of biomechanical response to tibial component malrotation remains difficult with musculoskeletal multibody dynamics models. This study compares a deep learning approach with four conventional machine learning methods to predict knee biomechanics during walking. Knee contact forces and kinematics were computed across malrotations within ±5° using multibody modeling, then used to train the models. Results show the deep learning method achieves higher accuracy for contact forces and kinematics, enabling fast calibration guidance for surgeons and robotic surgical navigation.","TYPE Original Research PUBLISHED 08 January 2024 DOI 10.3389/fbioe.2023.1255625  \nOPEN ACCESS  \nEDITED BY  \nJoão Manuel R. S. Tavares, University of Porto, Portugal  \nREVIEWED BY  \nXiaogang Wu,  \nTaiyuan University of Technology, China Anthony J. Petrella,  \nColorado School of Mines, United States  \n*CORRESPONDENCE  \nQida Zhang,  \n [zhangqida621@163.com](zhangqida621@163.com)  \nRECEIVED 09 July 2023  \nACCEPTED 21 December 2023  \nPUBLISHED 08 January 2024  \nCITATION  \nZhang Q, Li Z, Chen Z, Peng Y, Jin Z and Qin L (2024), Prediction of knee biomechanics with different tibial component malrotations after total knee arthroplasty: conventional machine learning vs. deep learning.  \nFront. Bioeng. Biotechnol. 11:1255625 .  \ndoi: 10.3389/fbioe.2023.1255625  \nCOPYRIGHT  \n© 2024 Zhang, Li, Chen, Peng, Jin and Qin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nPrediction of knee biomechanics with different tibial component malrotations after total knee arthroplasty: conventional machine learning vs.  \ndeep learning  \nQida Zhang 1*, Zhuhuan Li 2, Zhenxian Chen 3, Yinghu Peng 4, Zhongmin Jin 5,6 and Ling Qin 1  \n1Musculoskeletal Research Laboratory, Department of Orthopaedics and Traumatology, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China, 2State Key Laboratory for Manufacturing System Engineering, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China, 3Key Laboratory of Road Construction Technology and Equipment (Ministry of Education), School of Mechanical Engineering, Chang’an University, Xi’an, China, 4CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences, Shenzhen, China, 5Tribology Research Institute, School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China, 6Institute of Medical and Biological Engineering, School of Mechanical Engineering, University of Leeds, Leeds, United Kingdom  \nThe precise alignment of tibiofemoral components in total knee arthroplasty is a crucial factor in enhancing the longevity and functionality of the knee. However, it is a substantial challenge to quickly predict the biomechanical response tomalrotation of tibiofemoral components after total knee arthroplasty using musculoskeletal multibody dynamics models. The objective of the present study was to conduct a comparative analysis between a deep learning method and four conventional machine learning methods for predicting knee biomechanics with different tibial component malrotation during a walking gait after total knee arthroplasty. First, the knee contact forces and kinematics with different tibial component malrotation in the range of ±5° in the three directions of anterior/posterior slope, internal/external rotation, and varus/valgus rotation during a walking gait after total knee arthroplasty were calculated based on the developed musculoskeletal multibody dynamics model. Subsequently, deep learning and four conventional machine learning methods were developed using the above 343 sets of biomechanical data as the dataset. Finally, the results predicted by the deep learning method were compared to the results predicted by four conventional machine learning methods. The findings indicated that the deep learning method was more accurate than four conventional machine learning methods in predicting knee contact forces and kinematics with different tibial component malrotation during a walking gait after total knee arthroplasty. The deep learning method developed in this study enabled quickly determine the biom","cbCaiqB8E7A73c8l","https://ap.wps.com/l/cbCaiqB8E7A73c8l","pdf",1920034,1,12,"English","en",105,"# Introduction\n# Methods\n## Data generation with multibody dynamics\n## Machine learning models\n# Results\n## Prediction of knee contact forces\n## Prediction of knee kinematics\n# Discussion\n## Clinical and robotic navigation implications","[{\"question\":\"What problem does the study address in total knee arthroplasty?\",\"answer\":\"It addresses the challenge of quickly predicting knee biomechanical responses to tibial component malrotation after total knee arthroplasty, which is difficult with conventional musculoskeletal multibody dynamics workflows.\"},{\"question\":\"How was the dataset for model development generated?\",\"answer\":\"Knee contact forces and kinematics across tibial malrotation angles (±5° in three directions) during a walking gait were calculated using a developed musculoskeletal multibody dynamics model, producing 343 biomechanical data sets.\"},{\"question\":\"Which modeling approach performed best for predicting biomechanics?\",\"answer\":\"The deep learning method was more accurate than four conventional machine learning methods for predicting knee contact forces and kinematics under different tibial component malrotations.\"}]","Prediction of knee biomechanics with different tibial component malrotations after total knee arthroplasty - 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