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

• Published in: PeerJ (San Francisco, CA) CA), 2019-07, Vol.7, p.e7233-e7233, Article e7233
• Main Authors: Robles-Bykbaev, Yaroslava, Naya, Salvador, Díaz-Prado, Silvia, Calle-López, Daniel, Robles-Bykbaev, Vladimir, Garzón, Luis, Sanjurjo-Rodríguez, Clara, Tarrío-Saavedra, Javier
• Format: Article
• Language: English
• Subjects: Algorithms; Automation; Biodegradation; Biological activity; Biomaterials; Biotechnology; Bone growth; Cell differentiation; Cell growth; Colagen type I; Collagen; Collagen (type I); Data Mining and Machine Learning; Drugs and Devices; Entropy; Extracellular matrix; Image processing; Image segmentation; Learning algorithms; Machine learning; Mathematical models; Methods; Microscopy; NMR; Nuclear magnetic resonance; Orthopedics; Osteocytes; Prostate cancer; Regeneration; Researchers; Scanning electron microscopy; Statistical analysis; Statistical learning; Statistics; Stem cells
• ISSN: 2167-8359; 2167-8359
• DOI: 10.7717/peerj.7233

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.