Prediction of Knee Prosthesis Using Patient Gender and BMI With Non-marked X-Ray by Deep Learning.
deep learning
error correct output coding
prosthesis prediction
total knee arthroplasty
transfer learning
Journal
Frontiers in surgery
ISSN: 2296-875X
Titre abrégé: Front Surg
Pays: Switzerland
ID NLM: 101645127
Informations de publication
Date de publication:
2022
2022
Historique:
received:
20
10
2021
accepted:
11
02
2022
entrez:
1
4
2022
pubmed:
2
4
2022
medline:
2
4
2022
Statut:
epublish
Résumé
Total 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. In 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. The experimental results show the ECOC-based model reaches prediction accuracies of 88.23% and 86.27% for femoral components and tibial components, respectively. The 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.
Sections du résumé
Background
UNASSIGNED
Total 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.
Methods
UNASSIGNED
In 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.
Results
UNASSIGNED
The experimental results show the ECOC-based model reaches prediction accuracies of 88.23% and 86.27% for femoral components and tibial components, respectively.
Conclusions
UNASSIGNED
The 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.
Identifiants
pubmed: 35360429
doi: 10.3389/fsurg.2022.798761
pmc: PMC8963922
doi:
Types de publication
Journal Article
Langues
eng
Pagination
798761Informations de copyright
Copyright © 2022 Yue, Gao, Zhao, Li and Tian.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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