An artificial-vision- and statistical-learning-based method for studying the biodegradation of type I collagen scaffolds in bone regeneration systems

Abstract

This work proposes a method based on image analysis and machine and statistical learning to model and estimate osteocyte growth (in type I collagen scaffolds for bone regeneration systems) and the collagen degradation degree due to cellular growth. To achieve these aims, the mass of collagen -subjected to the action of osteocyte growth and differentiation from stem cells- was measured on 3 days during each of 2 months, under conditions simulating a tissue in the human body. In addition, optical microscopy was applied to obtain information about cellular growth, cellular differentiation, and collagen degradation. Our first contribution consists of the application of a supervised classification random forest algorithm to image texture features (the structure tensor and entropy) for estimating the different regions of interest in an image obtained by optical microscopy: the extracellular matrix, collagen, and image background, and nuclei. Then, extracellular-matrix and collagen regions of interest were determined by the extraction of features related to the progression of the cellular growth and collagen degradation (e.g., mean area of objects and the mode of an intensity histogram). Finally, these critical features were statistically modeled depending on time via nonparametric and parametric linear and nonlinear models such as those based on logistic functions. Namely, the parametric logistic mixture models provided a way to identify and model the degradation due to biological activity by estimating the corresponding proportion of mass loss. The relation between osteocyte growth and differentiation from stem cells, on the one hand, and collagen degradation, on the other hand, was determined too and modeled through analysis of image objects’ circularity and area, in addition to collagen mass loss. This set of imaging techniques, machine learning procedures, and statistical tools allowed us to characterize and parameterize type I collagen biodegradation when collagen acts as a scaffold in bone regeneration tasks. Namely, the parametric logistic mixture models provided a way to identify and model the degradation due to biological activity and thus to estimate the corresponding proportion of mass loss. Moreover, the proposed methodology can help to estimate the degradation degree of scaffolds from the information obtained by optical microscopy.

The preparation and application of calcium phosphate biomedical composites in filling of weight-bearing bone defects

“…Conversely, the liquid turned into a gel shape when the temperature rose above the PIT, and the process was reversible 12 , 13 . Collagen I was mainly a mineral secreted by osteoblasts, that could promote bone regeneration in bone tissue engineering 14 , 15 . As previously mentioned, CaP material was an excellent artificial bone: we speculated that the hardness and elasticity of the bio-composites would be improved with the help of thermosensitive hydrogel and collagen I.…”

Erosive Degradation Study of Concrete Augmented by Mussel Shells for Marine Construction

“…Namely, if we assume that there are different populations for which the regression trends are different (although they follow the same model type), we can estimate the population effects apart from the model residuals. In this work, the application of the so-called mixed effect regression models allows us to estimate the effect of the concrete formulation on the degradation and compare the degradation paths [34][35][36][37][38].…”

“…In addition to regression analysis, techniques of analysis of variance (ANOVA) have been applied to identify, with statistical evidence, those concrete formulations for which the erosive degradation paths are different [40,41]. When each experimental result (response variable) is a curve, as the present case, we can use all the information obtained in the laboratory by using functional data analysis (FDA) approaches for the ANOVA test, also called FANOVA [37]. In the current work, the application of FANOVA tests is recommended from the fact that a response variable is also functional: smooth curves of weight loss as a function of time.…”

“…Its probability distribution is estimated by nonparametric bootstrap procedures as shown in Cuevas et al [42] and Tarrío-Saavedra et al [37]. If V n is greater than a specific critical level V α , where α is the signification level, we will reject the null hypothesis, i.e., at least one group is different in terms of mass loss.…”

The future of bone regeneration: integrating AI into tissue engineering

“…Another interesting use of AI is a study that utilised an ensemble machine intelligence model, based on the random forest model, where hyper-parameters were semi-autonomously determined. This model predicted the dissolution behaviour (timedependent normalised critical ion mass loss) of various glasses with the Pearson correlation coefficient as high as 0.99 (it was highly successful) [134]. The success of this model was analysed to be due to the inherently nonlinear relationship between dissolution rate of silicate glasses and the pH of the contacting solution, alongside other variables in the system.…”