ArticleFrontiers in surgery2022
Prediction of Knee Prosthesis Using Patient Gender and BMI With Non-marked X-Ray by Deep Learning.
Article in Frontiers in surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 3 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The application of artificial intelligence in the design of highly compatible knee prostheses: a systematic review.BMC musculoskeletal disorders · 2026Pooled it
- 3D imaging-based AI models outperform demographic models and excel in tibial sizing compared with 2D models in total knee arthroplasty planning: A systematic review.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026Pooled it
- Application of artificial intelligence in X-ray imaging analysis for knee arthroplasty: A systematic review.PloS one · 2025Pooled it
- Article
- Development of an artificial intelligence model for predicting implant size in total knee arthroplasty using simple X-ray images.Journal of orthopaedic surgery and research · 2024Article
- Mixed Reality and Artificial Intelligence: A Holistic Approach to Multimodal Visualization and Extended Interaction in Knee Osteotomy.IEEE journal of translational engineering in health and medicine · 2024Article
- The Adherence of Digital Templating of Cemented Bicondylar Total Knee Arthroplasty Reveals Gender Differences.Journal of clinical medicine · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: 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: 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: 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: 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.