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Analysis of Trabecular Bone Mechanics Using Machine Learning
“Bone remodeling” is a dynamic process, and mutliphase analysis incorporated with the forecasting algorithm can help the biologists and orthopedics to interpret the laboratory generated results and to apply them in improving applications in the fields of “drug design, treatment, and therapy” of dise...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6434438/ https://www.ncbi.nlm.nih.gov/pubmed/30936677 http://dx.doi.org/10.1177/1176934318825084 |
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author | Sohail, Ayesha Younas, Muhammad Bhatti, Yousaf Li, Zhiwu Tunç, Sümeyye Abid, Muhammad |
author_facet | Sohail, Ayesha Younas, Muhammad Bhatti, Yousaf Li, Zhiwu Tunç, Sümeyye Abid, Muhammad |
author_sort | Sohail, Ayesha |
collection | PubMed |
description | “Bone remodeling” is a dynamic process, and mutliphase analysis incorporated with the forecasting algorithm can help the biologists and orthopedics to interpret the laboratory generated results and to apply them in improving applications in the fields of “drug design, treatment, and therapy” of diseased bones. The metastasized bone microenvironment has always remained a challenging puzzle for the researchers. A multiphase computational model is interfaced with the artificial intelligence algorithm in a hybrid manner during this research. Trabecular surface remodeling is presented in this article, with the aid of video graphic footage, and the associated parametric thresholds are derived from artificial intelligence and clinical data. |
format | Online Article Text |
id | pubmed-6434438 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-64344382019-04-01 Analysis of Trabecular Bone Mechanics Using Machine Learning Sohail, Ayesha Younas, Muhammad Bhatti, Yousaf Li, Zhiwu Tunç, Sümeyye Abid, Muhammad Evol Bioinform Online Original Research “Bone remodeling” is a dynamic process, and mutliphase analysis incorporated with the forecasting algorithm can help the biologists and orthopedics to interpret the laboratory generated results and to apply them in improving applications in the fields of “drug design, treatment, and therapy” of diseased bones. The metastasized bone microenvironment has always remained a challenging puzzle for the researchers. A multiphase computational model is interfaced with the artificial intelligence algorithm in a hybrid manner during this research. Trabecular surface remodeling is presented in this article, with the aid of video graphic footage, and the associated parametric thresholds are derived from artificial intelligence and clinical data. SAGE Publications 2019-03-24 /pmc/articles/PMC6434438/ /pubmed/30936677 http://dx.doi.org/10.1177/1176934318825084 Text en © The Author(s) 2019 http://www.creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Original Research Sohail, Ayesha Younas, Muhammad Bhatti, Yousaf Li, Zhiwu Tunç, Sümeyye Abid, Muhammad Analysis of Trabecular Bone Mechanics Using Machine Learning |
title | Analysis of Trabecular Bone Mechanics Using Machine Learning |
title_full | Analysis of Trabecular Bone Mechanics Using Machine Learning |
title_fullStr | Analysis of Trabecular Bone Mechanics Using Machine Learning |
title_full_unstemmed | Analysis of Trabecular Bone Mechanics Using Machine Learning |
title_short | Analysis of Trabecular Bone Mechanics Using Machine Learning |
title_sort | analysis of trabecular bone mechanics using machine learning |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6434438/ https://www.ncbi.nlm.nih.gov/pubmed/30936677 http://dx.doi.org/10.1177/1176934318825084 |
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