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

ABSTRACT: Highlights • Machine learning approaches allow for the simultaneous analysis to an entire microCT dataset to minimize bias and demonstrated that collective microarchitectural changes.• K-Means clusters and Support Vector Machine classification visualization provide intuitive interpretations of the differences in bone structure and microarchitecture between groups.• These techniques are complimentary to common statistical testing and provide additional ways of showing differences between microCT outcomes. 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. Graphical abstract Image, graphical abstract

SUBMITTER: Coulombe J 

PROVIDER: S-EPMC8563473 | biostudies-literature |

REPOSITORIES: biostudies-literature