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.

Cortical bone relationships are maintained regardless of sex and diet in a large population of LGXSM advanced intercross mice

“…For example, for the LG,SM AI mice, we reported 25 bone traits (14 for the radius, 11 for the femur) for 1113 animals from four experimental groups. Coulombe et al recently highlighted limitations in comparing groups based on individual bone traits ( Coulombe et al, 2021a ). As an alternative, they proposed principal component analysis (PCA), k-means clustering, and Support Vector Machine classification (SVM) as complimentary methods to concurrently evaluate all traits within a data set.…”

“…As an alternative, they proposed principal component analysis (PCA), k-means clustering, and Support Vector Machine classification (SVM) as complimentary methods to concurrently evaluate all traits within a data set. Specifically, PCA was used to explain the variation of bone trait values within the population using a smaller number of independent variables resulting in three principal components that explain over 90 % of the population variation in ten individual traits ( Coulombe et al, 2021a ; Coulombe et al, 2021b ). Herein, we apply some of these approaches to the LG,SM AI data set to identify a reduced set of traits that still captures the variation in morphology or mechanical properties between animals.…”

Multi-scale cortical bone traits vary in females and males from two mouse models of genetic diversity

“…Principal component analysis (PCA) is a method for dimensional reduction that can facilitate between-group comparisons in studies that assess numerous bone traits. 22 , 28 , 29 PCA was performed using R on 2 datasets from the inbred founder population: (1) 15 radial and whole body traits, and (2) 10 lacunar traits. Each trait was centered and scaled within the prcomp function (mean = 0, SD = 1), and its contribution to each principal component (PC) was determined.…”