Application of machine learning classifiers for microcomputed tomography data assessment of mouse bone microarchitecture.

Autor:	Coulombe JC; Department of Mechanical Engineering, UCB 427, University of Colorado, Boulder, CO 80309, United States of America.; BioFrontiers Institute, UCB 596, University of Colorado, Boulder, CO 80309, United States of America., Mullen ZK; Laboratory for Interdisciplinary Statistical Analysis / Department of Computer Science, UCB 427, University of Colorado, Boulder, CO 80309, United States of America., Lynch ME; Department of Mechanical Engineering, UCB 427, University of Colorado, Boulder, CO 80309, United States of America.; BioFrontiers Institute, UCB 596, University of Colorado, Boulder, CO 80309, United States of America., Stodieck LS; Aerospace Engineering Sciences / BioServe Space Technologies, UCB 429, University of Colorado, Boulder, CO 80309, United States of America., Ferguson VL; Department of Mechanical Engineering, UCB 427, University of Colorado, Boulder, CO 80309, United States of America.; BioFrontiers Institute, UCB 596, University of Colorado, Boulder, CO 80309, United States of America.; Aerospace Engineering Sciences / BioServe Space Technologies, UCB 429, University of Colorado, Boulder, CO 80309, United States of America.
Jazyk:	angličtina
Zdroj:	MethodsX [MethodsX] 2021 Aug 24; Vol. 8, pp. 101497. Date of Electronic Publication: 2021 Aug 24 (Print Publication: 2021).
DOI:	10.1016/j.mex.2021.101497
Abstrakt:	The current standard approach for analyzing cortical bone structure and trabecular bone microarchitecture from micro-computed tomography (microCT) is through classic parametric (e.g., ANOVA, Student's T-test) and nonparametric (e.g., Mann-Whitney U test) statistical tests and the reporting of p -values to indicate significance. However, on their own, these univariate assessments of significance fall prey to a number of weaknesses, including an increased chance of Type 1 error from multiple comparisons. Machine learning classification methods (e.g., unsupervised, k-means cluster analysis and supervised Support Vector Machine classification, SVM) simultaneously utilize an entire dataset comprised of many cortical structure or trabecular microarchitecture measures, thus minimizing bias and Type 1 error that are generated through multiple testing. Through simultaneous evaluation of an entire dataset, k-means and SVM thus provide a complementary approach to classic statistical analysis and enable a more robust assessment of microCT measures. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (© 2021 The Authors. Published by Elsevier B.V.)
Databáze:	MEDLINE
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