Prediction of knee biomechanics with different tibial component malrotations after total knee arthroplasty: conventional machine learning vs. deep learning

The 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 to malrotation of tibiofemoral components after total knee ar...

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Bibliographic Details
Published in:	Frontiers in bioengineering and biotechnology Vol. 11; p. 1255625
Main Authors:	Zhang, Qida, Li, Zhuhuan, Chen, Zhenxian, Peng, Yinghu, Jin, Zhongmin, Qin, Ling
Format:	Journal Article
Language:	English
Published:	Switzerland Frontiers Media S.A 08-01-2024
Subjects:	accurate rotational alignment Bioengineering and Biotechnology biomechanics deep learning machine learning musculoskeletal multibody dynamics model total knee arthroplasty deep learning total knee arthroplasty biomechanics accurate rotational alignment machine learning musculoskeletal multibody dynamics model
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Summary:	The 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 to malrotation 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 biomechanical response with different tibial component malrotation during a walking gait after total knee arthroplasty. The proposed method offered surgeons and surgical robots the ability to establish a calibration safety zone, which was essential for achieving precise alignment in both preoperative surgical planning and intraoperative robotic-assisted surgical navigation.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Edited by: João Manuel R. S. Tavares, University of Porto, Portugal Reviewed by: Xiaogang Wu, Taiyuan University of Technology, China Anthony J. Petrella, Colorado School of Mines, United States
ISSN:	2296-4185 2296-4185
DOI:	10.3389/fbioe.2023.1255625