Prediction of Knee Prosthesis Using Patient Gender and BMI With Non-marked X-Ray by Deep Learning

BackgroundTotal knee arthroplasty (TKA) is effective for severe osteoarthritis and other related diseases. Accurate prosthesis prediction is a crucial factor for improving clinical outcomes and patient satisfaction after TKA. Current studies mainly focus on conventional manual template measurements,...
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Autor*in:	Yu Yue [verfasserIn] Qiaochu Gao [verfasserIn] Minwei Zhao [verfasserIn] Dou Li [verfasserIn] Hua Tian [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	total knee arthroplasty prosthesis prediction deep learning error correct output coding transfer learning

Übergeordnetes Werk:	In: Frontiers in Surgery - Frontiers Media S.A., 2014, 9(2022)
Übergeordnetes Werk:	volume:9 ; year:2022

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fsurg.2022.798761

Katalog-ID:	DOAJ016559983

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520			\|a BackgroundTotal knee arthroplasty (TKA) is effective for severe osteoarthritis and other related diseases. Accurate prosthesis prediction is a crucial factor for improving clinical outcomes and patient satisfaction after TKA. Current studies mainly focus on conventional manual template measurements, which are inconvenient and inefficient.MethodsIn this article, we utilize convolutional neural networks to analyze a multimodal patient data and design a system that helps doctors choose prostheses for TKA. To alleviate the problems of insufficient data and uneven distribution of labels, research on model structure, loss function and transfer learning is carried out. Algorithm optimization based on error correct output coding (ECOC) is implemented to further boost the performance.ResultsThe experimental results show the ECOC-based model reaches prediction accuracies of 88.23% and 86.27% for femoral components and tibial components, respectively.ConclusionsThe results verify that the ECOC-based model for prosthesis prediction in TKA is feasible and outperforms existing methods, which is of great significance for templating.
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allfields	10.3389/fsurg.2022.798761 doi (DE-627)DOAJ016559983 (DE-599)DOAJ5255b506e165449a8a4b87ca1096694c DE-627 ger DE-627 rakwb eng RD1-811 Yu Yue verfasserin aut Prediction of Knee Prosthesis Using Patient Gender and BMI With Non-marked X-Ray by Deep Learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundTotal knee arthroplasty (TKA) is effective for severe osteoarthritis and other related diseases. Accurate prosthesis prediction is a crucial factor for improving clinical outcomes and patient satisfaction after TKA. Current studies mainly focus on conventional manual template measurements, which are inconvenient and inefficient.MethodsIn this article, we utilize convolutional neural networks to analyze a multimodal patient data and design a system that helps doctors choose prostheses for TKA. To alleviate the problems of insufficient data and uneven distribution of labels, research on model structure, loss function and transfer learning is carried out. Algorithm optimization based on error correct output coding (ECOC) is implemented to further boost the performance.ResultsThe experimental results show the ECOC-based model reaches prediction accuracies of 88.23% and 86.27% for femoral components and tibial components, respectively.ConclusionsThe results verify that the ECOC-based model for prosthesis prediction in TKA is feasible and outperforms existing methods, which is of great significance for templating. total knee arthroplasty prosthesis prediction deep learning error correct output coding transfer learning Surgery Qiaochu Gao verfasserin aut Minwei Zhao verfasserin aut Dou Li verfasserin aut Hua Tian verfasserin aut In Frontiers in Surgery Frontiers Media S.A., 2014 9(2022) (DE-627)788288954 (DE-600)2773823-1 2296875X nnns volume:9 year:2022 https://doi.org/10.3389/fsurg.2022.798761 kostenfrei https://doaj.org/article/5255b506e165449a8a4b87ca1096694c kostenfrei https://www.frontiersin.org/articles/10.3389/fsurg.2022.798761/full kostenfrei https://doaj.org/toc/2296-875X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_2446 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022
spelling	10.3389/fsurg.2022.798761 doi (DE-627)DOAJ016559983 (DE-599)DOAJ5255b506e165449a8a4b87ca1096694c DE-627 ger DE-627 rakwb eng RD1-811 Yu Yue verfasserin aut Prediction of Knee Prosthesis Using Patient Gender and BMI With Non-marked X-Ray by Deep Learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundTotal knee arthroplasty (TKA) is effective for severe osteoarthritis and other related diseases. Accurate prosthesis prediction is a crucial factor for improving clinical outcomes and patient satisfaction after TKA. Current studies mainly focus on conventional manual template measurements, which are inconvenient and inefficient.MethodsIn this article, we utilize convolutional neural networks to analyze a multimodal patient data and design a system that helps doctors choose prostheses for TKA. To alleviate the problems of insufficient data and uneven distribution of labels, research on model structure, loss function and transfer learning is carried out. Algorithm optimization based on error correct output coding (ECOC) is implemented to further boost the performance.ResultsThe experimental results show the ECOC-based model reaches prediction accuracies of 88.23% and 86.27% for femoral components and tibial components, respectively.ConclusionsThe results verify that the ECOC-based model for prosthesis prediction in TKA is feasible and outperforms existing methods, which is of great significance for templating. total knee arthroplasty prosthesis prediction deep learning error correct output coding transfer learning Surgery Qiaochu Gao verfasserin aut Minwei Zhao verfasserin aut Dou Li verfasserin aut Hua Tian verfasserin aut In Frontiers in Surgery Frontiers Media S.A., 2014 9(2022) (DE-627)788288954 (DE-600)2773823-1 2296875X nnns volume:9 year:2022 https://doi.org/10.3389/fsurg.2022.798761 kostenfrei https://doaj.org/article/5255b506e165449a8a4b87ca1096694c kostenfrei https://www.frontiersin.org/articles/10.3389/fsurg.2022.798761/full kostenfrei https://doaj.org/toc/2296-875X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_2446 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022
allfields_unstemmed	10.3389/fsurg.2022.798761 doi (DE-627)DOAJ016559983 (DE-599)DOAJ5255b506e165449a8a4b87ca1096694c DE-627 ger DE-627 rakwb eng RD1-811 Yu Yue verfasserin aut Prediction of Knee Prosthesis Using Patient Gender and BMI With Non-marked X-Ray by Deep Learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundTotal knee arthroplasty (TKA) is effective for severe osteoarthritis and other related diseases. Accurate prosthesis prediction is a crucial factor for improving clinical outcomes and patient satisfaction after TKA. Current studies mainly focus on conventional manual template measurements, which are inconvenient and inefficient.MethodsIn this article, we utilize convolutional neural networks to analyze a multimodal patient data and design a system that helps doctors choose prostheses for TKA. To alleviate the problems of insufficient data and uneven distribution of labels, research on model structure, loss function and transfer learning is carried out. Algorithm optimization based on error correct output coding (ECOC) is implemented to further boost the performance.ResultsThe experimental results show the ECOC-based model reaches prediction accuracies of 88.23% and 86.27% for femoral components and tibial components, respectively.ConclusionsThe results verify that the ECOC-based model for prosthesis prediction in TKA is feasible and outperforms existing methods, which is of great significance for templating. total knee arthroplasty prosthesis prediction deep learning error correct output coding transfer learning Surgery Qiaochu Gao verfasserin aut Minwei Zhao verfasserin aut Dou Li verfasserin aut Hua Tian verfasserin aut In Frontiers in Surgery Frontiers Media S.A., 2014 9(2022) (DE-627)788288954 (DE-600)2773823-1 2296875X nnns volume:9 year:2022 https://doi.org/10.3389/fsurg.2022.798761 kostenfrei https://doaj.org/article/5255b506e165449a8a4b87ca1096694c kostenfrei https://www.frontiersin.org/articles/10.3389/fsurg.2022.798761/full kostenfrei https://doaj.org/toc/2296-875X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_2446 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022
allfieldsGer	10.3389/fsurg.2022.798761 doi (DE-627)DOAJ016559983 (DE-599)DOAJ5255b506e165449a8a4b87ca1096694c DE-627 ger DE-627 rakwb eng RD1-811 Yu Yue verfasserin aut Prediction of Knee Prosthesis Using Patient Gender and BMI With Non-marked X-Ray by Deep Learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundTotal knee arthroplasty (TKA) is effective for severe osteoarthritis and other related diseases. Accurate prosthesis prediction is a crucial factor for improving clinical outcomes and patient satisfaction after TKA. Current studies mainly focus on conventional manual template measurements, which are inconvenient and inefficient.MethodsIn this article, we utilize convolutional neural networks to analyze a multimodal patient data and design a system that helps doctors choose prostheses for TKA. To alleviate the problems of insufficient data and uneven distribution of labels, research on model structure, loss function and transfer learning is carried out. Algorithm optimization based on error correct output coding (ECOC) is implemented to further boost the performance.ResultsThe experimental results show the ECOC-based model reaches prediction accuracies of 88.23% and 86.27% for femoral components and tibial components, respectively.ConclusionsThe results verify that the ECOC-based model for prosthesis prediction in TKA is feasible and outperforms existing methods, which is of great significance for templating. total knee arthroplasty prosthesis prediction deep learning error correct output coding transfer learning Surgery Qiaochu Gao verfasserin aut Minwei Zhao verfasserin aut Dou Li verfasserin aut Hua Tian verfasserin aut In Frontiers in Surgery Frontiers Media S.A., 2014 9(2022) (DE-627)788288954 (DE-600)2773823-1 2296875X nnns volume:9 year:2022 https://doi.org/10.3389/fsurg.2022.798761 kostenfrei https://doaj.org/article/5255b506e165449a8a4b87ca1096694c kostenfrei https://www.frontiersin.org/articles/10.3389/fsurg.2022.798761/full kostenfrei https://doaj.org/toc/2296-875X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_2446 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022
allfieldsSound	10.3389/fsurg.2022.798761 doi (DE-627)DOAJ016559983 (DE-599)DOAJ5255b506e165449a8a4b87ca1096694c DE-627 ger DE-627 rakwb eng RD1-811 Yu Yue verfasserin aut Prediction of Knee Prosthesis Using Patient Gender and BMI With Non-marked X-Ray by Deep Learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundTotal knee arthroplasty (TKA) is effective for severe osteoarthritis and other related diseases. Accurate prosthesis prediction is a crucial factor for improving clinical outcomes and patient satisfaction after TKA. Current studies mainly focus on conventional manual template measurements, which are inconvenient and inefficient.MethodsIn this article, we utilize convolutional neural networks to analyze a multimodal patient data and design a system that helps doctors choose prostheses for TKA. To alleviate the problems of insufficient data and uneven distribution of labels, research on model structure, loss function and transfer learning is carried out. Algorithm optimization based on error correct output coding (ECOC) is implemented to further boost the performance.ResultsThe experimental results show the ECOC-based model reaches prediction accuracies of 88.23% and 86.27% for femoral components and tibial components, respectively.ConclusionsThe results verify that the ECOC-based model for prosthesis prediction in TKA is feasible and outperforms existing methods, which is of great significance for templating. total knee arthroplasty prosthesis prediction deep learning error correct output coding transfer learning Surgery Qiaochu Gao verfasserin aut Minwei Zhao verfasserin aut Dou Li verfasserin aut Hua Tian verfasserin aut In Frontiers in Surgery Frontiers Media S.A., 2014 9(2022) (DE-627)788288954 (DE-600)2773823-1 2296875X nnns volume:9 year:2022 https://doi.org/10.3389/fsurg.2022.798761 kostenfrei https://doaj.org/article/5255b506e165449a8a4b87ca1096694c kostenfrei https://www.frontiersin.org/articles/10.3389/fsurg.2022.798761/full kostenfrei https://doaj.org/toc/2296-875X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_2446 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022
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Accurate prosthesis prediction is a crucial factor for improving clinical outcomes and patient satisfaction after TKA. Current studies mainly focus on conventional manual template measurements, which are inconvenient and inefficient.MethodsIn this article, we utilize convolutional neural networks to analyze a multimodal patient data and design a system that helps doctors choose prostheses for TKA. To alleviate the problems of insufficient data and uneven distribution of labels, research on model structure, loss function and transfer learning is carried out. Algorithm optimization based on error correct output coding (ECOC) is implemented to further boost the performance.ResultsThe experimental results show the ECOC-based model reaches prediction accuracies of 88.23% and 86.27% for femoral components and tibial components, respectively.ConclusionsThe results verify that the ECOC-based model for prosthesis prediction in TKA is feasible and outperforms existing methods, which is of great significance for templating.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">total knee arthroplasty</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">prosthesis prediction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">deep learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">error correct output coding</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">transfer 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callnumber-first	R - Medicine
author	Yu Yue
spellingShingle	Yu Yue misc RD1-811 misc total knee arthroplasty misc prosthesis prediction misc deep learning misc error correct output coding misc transfer learning misc Surgery Prediction of Knee Prosthesis Using Patient Gender and BMI With Non-marked X-Ray by Deep Learning
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topic_title	RD1-811 Prediction of Knee Prosthesis Using Patient Gender and BMI With Non-marked X-Ray by Deep Learning total knee arthroplasty prosthesis prediction deep learning error correct output coding transfer learning
topic	misc RD1-811 misc total knee arthroplasty misc prosthesis prediction misc deep learning misc error correct output coding misc transfer learning misc Surgery
topic_unstemmed	misc RD1-811 misc total knee arthroplasty misc prosthesis prediction misc deep learning misc error correct output coding misc transfer learning misc Surgery
topic_browse	misc RD1-811 misc total knee arthroplasty misc prosthesis prediction misc deep learning misc error correct output coding misc transfer learning misc Surgery
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title	Prediction of Knee Prosthesis Using Patient Gender and BMI With Non-marked X-Ray by Deep Learning
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title_full	Prediction of Knee Prosthesis Using Patient Gender and BMI With Non-marked X-Ray by Deep Learning
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title_sort	prediction of knee prosthesis using patient gender and bmi with non-marked x-ray by deep learning
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title_auth	Prediction of Knee Prosthesis Using Patient Gender and BMI With Non-marked X-Ray by Deep Learning
abstract	BackgroundTotal knee arthroplasty (TKA) is effective for severe osteoarthritis and other related diseases. Accurate prosthesis prediction is a crucial factor for improving clinical outcomes and patient satisfaction after TKA. Current studies mainly focus on conventional manual template measurements, which are inconvenient and inefficient.MethodsIn this article, we utilize convolutional neural networks to analyze a multimodal patient data and design a system that helps doctors choose prostheses for TKA. To alleviate the problems of insufficient data and uneven distribution of labels, research on model structure, loss function and transfer learning is carried out. Algorithm optimization based on error correct output coding (ECOC) is implemented to further boost the performance.ResultsThe experimental results show the ECOC-based model reaches prediction accuracies of 88.23% and 86.27% for femoral components and tibial components, respectively.ConclusionsThe results verify that the ECOC-based model for prosthesis prediction in TKA is feasible and outperforms existing methods, which is of great significance for templating.
abstractGer	BackgroundTotal knee arthroplasty (TKA) is effective for severe osteoarthritis and other related diseases. Accurate prosthesis prediction is a crucial factor for improving clinical outcomes and patient satisfaction after TKA. Current studies mainly focus on conventional manual template measurements, which are inconvenient and inefficient.MethodsIn this article, we utilize convolutional neural networks to analyze a multimodal patient data and design a system that helps doctors choose prostheses for TKA. To alleviate the problems of insufficient data and uneven distribution of labels, research on model structure, loss function and transfer learning is carried out. Algorithm optimization based on error correct output coding (ECOC) is implemented to further boost the performance.ResultsThe experimental results show the ECOC-based model reaches prediction accuracies of 88.23% and 86.27% for femoral components and tibial components, respectively.ConclusionsThe results verify that the ECOC-based model for prosthesis prediction in TKA is feasible and outperforms existing methods, which is of great significance for templating.
abstract_unstemmed	BackgroundTotal knee arthroplasty (TKA) is effective for severe osteoarthritis and other related diseases. Accurate prosthesis prediction is a crucial factor for improving clinical outcomes and patient satisfaction after TKA. Current studies mainly focus on conventional manual template measurements, which are inconvenient and inefficient.MethodsIn this article, we utilize convolutional neural networks to analyze a multimodal patient data and design a system that helps doctors choose prostheses for TKA. To alleviate the problems of insufficient data and uneven distribution of labels, research on model structure, loss function and transfer learning is carried out. Algorithm optimization based on error correct output coding (ECOC) is implemented to further boost the performance.ResultsThe experimental results show the ECOC-based model reaches prediction accuracies of 88.23% and 86.27% for femoral components and tibial components, respectively.ConclusionsThe results verify that the ECOC-based model for prosthesis prediction in TKA is feasible and outperforms existing methods, which is of great significance for templating.
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title_short	Prediction of Knee Prosthesis Using Patient Gender and BMI With Non-marked X-Ray by Deep Learning
url	https://doi.org/10.3389/fsurg.2022.798761 https://doaj.org/article/5255b506e165449a8a4b87ca1096694c https://www.frontiersin.org/articles/10.3389/fsurg.2022.798761/full https://doaj.org/toc/2296-875X
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Accurate prosthesis prediction is a crucial factor for improving clinical outcomes and patient satisfaction after TKA. Current studies mainly focus on conventional manual template measurements, which are inconvenient and inefficient.MethodsIn this article, we utilize convolutional neural networks to analyze a multimodal patient data and design a system that helps doctors choose prostheses for TKA. To alleviate the problems of insufficient data and uneven distribution of labels, research on model structure, loss function and transfer learning is carried out. Algorithm optimization based on error correct output coding (ECOC) is implemented to further boost the performance.ResultsThe experimental results show the ECOC-based model reaches prediction accuracies of 88.23% and 86.27% for femoral components and tibial components, respectively.ConclusionsThe results verify that the ECOC-based model for prosthesis prediction in TKA is feasible and outperforms existing methods, which is of great significance for templating.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">total knee arthroplasty</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">prosthesis prediction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">deep learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">error correct output coding</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">transfer 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