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| 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 |
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| 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 | Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
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 |
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| 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 |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 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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| Speaker | Author-1 |
| Date Time | 2018-07-24 13:55:00 |
| Presentation Time | 20 minutes |
| Registration for | MI |
| Paper # | MI2018-26 |
| Volume (vol) | vol.118 |
| Number (no) | no.150 |
| Page | pp.21-24 |
| #Pages | 4 |
| Date of Issue | 2018-07-17 (MI) |
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