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A Machine-Learning-Based Approach for Predicting Mechanical Performance of Semi-Porous Hip Stems
Novel designs of porous and semi-porous hip stems attempt to alleviate complications such as aseptic loosening, stress shielding, and eventual implant failure. Various designs of hip stems are modeled to simulate biomechanical performance using finite element analysis; however, these models are comp...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10054603/ https://www.ncbi.nlm.nih.gov/pubmed/36976080 http://dx.doi.org/10.3390/jfb14030156 |
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author | Akkad, Khaled Mehboob, Hassan Alyamani, Rakan Tarlochan, Faris |
author_facet | Akkad, Khaled Mehboob, Hassan Alyamani, Rakan Tarlochan, Faris |
author_sort | Akkad, Khaled |
collection | PubMed |
description | Novel designs of porous and semi-porous hip stems attempt to alleviate complications such as aseptic loosening, stress shielding, and eventual implant failure. Various designs of hip stems are modeled to simulate biomechanical performance using finite element analysis; however, these models are computationally expensive. Therefore, the machine learning approach is incorporated with simulated data to predict the new biomechanical performance of new designs of hip stems. Six types of algorithms based on machine learning were employed to validate the simulated results of finite element analysis. Afterwards, new designs of semi-porous stems with outer dense layers of 2.5 and 3 mm and porosities of 10–80% were used to predict the stiffness of the stems, stresses in outer dense layers, stresses in porous sections, and factor of safety under physiological loads using machine learning algorithms. It was determined that decision tree regression is the top-performing machine learning algorithm as per the used simulation data in terms of the validation mean absolute percentage error which equals 19.62%. It was also found that ridge regression produces the most consistent test set trend as compared with the original simulated finite element analysis results despite relying on a relatively small data set. These predicted results employing trained algorithms provided the understanding that changing the design parameters of semi-porous stems affects the biomechanical performance without carrying out finite element analysis. |
format | Online Article Text |
id | pubmed-10054603 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100546032023-03-30 A Machine-Learning-Based Approach for Predicting Mechanical Performance of Semi-Porous Hip Stems Akkad, Khaled Mehboob, Hassan Alyamani, Rakan Tarlochan, Faris J Funct Biomater Article Novel designs of porous and semi-porous hip stems attempt to alleviate complications such as aseptic loosening, stress shielding, and eventual implant failure. Various designs of hip stems are modeled to simulate biomechanical performance using finite element analysis; however, these models are computationally expensive. Therefore, the machine learning approach is incorporated with simulated data to predict the new biomechanical performance of new designs of hip stems. Six types of algorithms based on machine learning were employed to validate the simulated results of finite element analysis. Afterwards, new designs of semi-porous stems with outer dense layers of 2.5 and 3 mm and porosities of 10–80% were used to predict the stiffness of the stems, stresses in outer dense layers, stresses in porous sections, and factor of safety under physiological loads using machine learning algorithms. It was determined that decision tree regression is the top-performing machine learning algorithm as per the used simulation data in terms of the validation mean absolute percentage error which equals 19.62%. It was also found that ridge regression produces the most consistent test set trend as compared with the original simulated finite element analysis results despite relying on a relatively small data set. These predicted results employing trained algorithms provided the understanding that changing the design parameters of semi-porous stems affects the biomechanical performance without carrying out finite element analysis. MDPI 2023-03-15 /pmc/articles/PMC10054603/ /pubmed/36976080 http://dx.doi.org/10.3390/jfb14030156 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Akkad, Khaled Mehboob, Hassan Alyamani, Rakan Tarlochan, Faris A Machine-Learning-Based Approach for Predicting Mechanical Performance of Semi-Porous Hip Stems |
title | A Machine-Learning-Based Approach for Predicting Mechanical Performance of Semi-Porous Hip Stems |
title_full | A Machine-Learning-Based Approach for Predicting Mechanical Performance of Semi-Porous Hip Stems |
title_fullStr | A Machine-Learning-Based Approach for Predicting Mechanical Performance of Semi-Porous Hip Stems |
title_full_unstemmed | A Machine-Learning-Based Approach for Predicting Mechanical Performance of Semi-Porous Hip Stems |
title_short | A Machine-Learning-Based Approach for Predicting Mechanical Performance of Semi-Porous Hip Stems |
title_sort | machine-learning-based approach for predicting mechanical performance of semi-porous hip stems |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10054603/ https://www.ncbi.nlm.nih.gov/pubmed/36976080 http://dx.doi.org/10.3390/jfb14030156 |
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