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Paper Abstract and Keywords
Presentation 2018-07-24 13:55
Machine learning for estimating implanted knee functions using a CT-free navigation
Belayat Hossain (UHyogo), Takatoshi Morooka, Makiko Okuno (Hyogo C. Medicine), Manabu Nii (UHyogo), Shinichi Yoshiya (Hyogo C. Medicine), Syoji Kobashi (UHyogo) MI2018-26
Abstract (in Japanese) (See Japanese page) 
(in English) In total knee arthroplasty (TKA), the damaged knee joint is replaced by artificial prosthesis. Patient-specific TKA surgical planning require evaluation of prosthesis because outcome of the TKA strongly depends on types of prosthesis and surgical methods, and it also differs from subject to subject. Machine learning (ML) techniques could be used to predict postoperative knee kinematics by utilizing a set of pairs of the clinical pre- and postoperative data. This study finds out the feasibility of the support vector regression (SVR) for clinical study, especially in Orthopaedics, and then its performance is compared to other ML method such as neural network (NN), generalized linear regression (GLR) to find the best ML method. It was found that the model?s prediction performance slightly differs from other ML methods. Therefore, this study recommends choosing the best ML methods (GLM and NN) with high accuracy for predictive model construction for predicting TKA outcome.
Keyword (in Japanese) (See Japanese page) 
(in English) Knee Implantation / Total knee arthroplasty / Kinematics / Machine learning / Predictive model / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 150, MI2018-26, pp. 21-24, July 2018.
Paper # MI2018-26 
Date of Issue 2018-07-17 (MI) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF MI2018-26

Conference Information
Committee MI  
Conference Date 2018-07-24 - 2018-07-24 
Place (in Japanese) (See Japanese page) 
Place (in English) aiina (Morioka, Iwate) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical Imaging, etc. 
Paper Information
Registration To MI 
Conference Code 2018-07-MI 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Machine learning for estimating implanted knee functions using a CT-free navigation 
Sub Title (in English)  
Keyword(1) Knee Implantation  
Keyword(2) Total knee arthroplasty  
Keyword(3) Kinematics  
Keyword(4) Machine learning  
Keyword(5) Predictive model  
1st Author's Name Belayat Hossain  
1st Author's Affiliation University of Hyogo (UHyogo)
2nd Author's Name Takatoshi Morooka  
2nd Author's Affiliation Hyogo College of Medicine (Hyogo C. Medicine)
3rd Author's Name Makiko Okuno  
3rd Author's Affiliation Hyogo College of Medicine (Hyogo C. Medicine)
4th Author's Name Manabu Nii  
4th Author's Affiliation University of Hyogo (UHyogo)
5th Author's Name Shinichi Yoshiya  
5th Author's Affiliation Hyogo College of Medicine (Hyogo C. Medicine)
6th Author's Name Syoji Kobashi  
6th Author's Affiliation University of Hyogo (UHyogo)
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Date Time 2018-07-24 13:55:00 
Presentation Time 20 
Registration for MI 
Paper # IEICE-MI2018-26 
Volume (vol) IEICE-118 
Number (no) no.150 
Page pp.21-24 
#Pages IEICE-4 
Date of Issue IEICE-MI-2018-07-17 

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