Analysis of Trabecular Bone Mechanics Using Machine Learning

Development and Numerical Implementation of a Modified Mixed-Mode Traction–Separation Law for the Simulation of Interlaminar Fracture of Co-Consolidated Thermoplastic Laminates Considering the Effect of Fiber Bridging

In the present work, a numerical model based on the cohesive zone modeling (CZM) approach has been developed to simulate mixed-mode fracture of co-consolidated low melt polyaryletherketone thermoplastic laminates by considering fiber bridging. A modified traction separation law of a tri-linear form has been developed by superimposing the bi-linear behaviors of the matrix and fibers. Initially, the data from mode I (DCB) and mode II (ENF) fracture toughness tests were used to construct the R-curves of the joints in the opening and sliding directions. The constructed curves were incorporated into the numerical models employing a user-defined material subroutine developed in the LS-Dyna finite element (FE) code. A numerical method was used to extract the fiber bridging law directly from the simulation results, thus eliminating the need for the continuous monitoring of crack opening displacement during testing. The final cohesive model was implemented via two identical FE models to simulate the fracture of a Single-Lap-Shear specimen, in which a considerable amount of fiber bridging was observed on the fracture area. The numerical results showed that the developed model presented improved accuracy in comparison to the CZM with the bi-linear traction–separation law (T–SL) in terms of the predicted strength of the joint.

How Artificial Intelligence and Machine Learning Is Assisting Us to Extract Meaning from Data on Bone Mechanics?

Dramatic advancements in interdisciplinary research with the fourth paradigm of science, especially the implementation of computer science, nourish the potential for artificial intelligence (AI), machine learning (ML), and artificial neural network (ANN) algorithms to be applied to studies concerning mechanics of bones. Despite recent enormous advancement in techniques, gaining deep knowledge to find correlations between bone shape, material, mechanical, and physical responses as well as properties is a daunting task. This is due to both complexity of the material itself and the convoluted shapes that this complex material forms. Moreover, many uncertainties and ambiguities exist concerning the use of traditional computational techniques that hinders gaining a full comprehension of this advanced biological material. This book chapter offers a review of literature on the use of AI, ML, and ANN in the study of bone mechanics research. A main question as to why to implement AI and ML in the mechanics of bones is fully addressed and explained. This chapter also introduces AI and ML and elaborates on the main features of ML algorithms such as learning paradigms, subtypes, main ideas with examples, performance metrics, training algorithms, and training datasets. As a frequently employed ML algorithm in bone mechanics, feedforward ANNs are discussed to make their taxonomy and working principles more readily comprehensible to researchers. A summary as well as detailed review of papers that employed ANNs to learn from collected data on bone mechanics are presented. Reviewing literature on the use of these data-driven tools is essential since their wider application has the potential to: improve clinical assessments enabling real-time simulations; avoid and/or minimize injuries; and, encourage early detection of such injuries in the first place.

A machine learning approach to investigate the materials science of enamel aging

Understanding aging of tooth tissues is critical to the development of patient-centric oral healthcare. Yet, the traditional methods for analyzing the composition-structure-property relationships of hard tissues have limitations when considering aging and other factors.

OBJECTIVE

To apply unsupervised machine learning tools to pursue an understanding of relationships between the composition and mechanical behavior of aging enamel.

METHODS

Molar teeth were collected from primary (age ≤ 8), young adult (24 ≤ age ≤ 46) and old adult (55 ≤ age) donors. The hardness and elastic modulus were quantified using nanoindentation as a function of distance from the Dentin Enamel Junction (DEJ) within the cervical, cuspal and inter-cuspal regions of the enamel crown. Similarly, a co-located analysis of the chemical composition and structure was performed using Raman spectroscopy. A Self-Organizing Maps (SOMs) algorithm was implemented to identify multi-dimensional composition-property relationships.

RESULTS

The hardness and elastic modulus are positively correlated to crystallinity and negatively correlated with carbonate substitution. Furthermore, the effects from fluoridation on the age-dependent properties of enamel is non-linear and depends on its location. The contributions of fluoridation to the enamel properties are different in the cervical and non-cervical regions and appear to be unique within primary and senior adult teeth.

SIGNIFICANCE

Based on the findings, unsupervised learning methods can reveal complicated non-linear structure-property relationships in tooth tissues and help to understand the materials science of aging and its consequences.

Machine learning classification models for fetal skeletal development performance prediction using maternal bone metabolic proteins in goats

Background

In developing countries, maternal undernutrition is the major intrauterine environmental factor contributing to fetal development and adverse pregnancy outcomes. Maternal nutrition restriction (MNR) in gestation has proven to impact overall growth, bone development, and proliferation and metabolism of mesenchymal stem cells in offspring. However, the efficient method for elucidation of fetal bone development performance through maternal bone metabolic biochemical markers remains elusive.

Methods

We adapted goats to elucidate fetal bone development state with maternal serum bone metabolic proteins under malnutrition conditions in mid- and late-gestation stages. We used the experimental data to create 72 datasets by mixing different input features such as one-hot encoding of experimental conditions, metabolic original data, experimental-centered features and experimental condition probabilities. Seven Machine Learning methods have been used to predict six fetal bone parameters (weight, length, and diameter of femur/humerus).

Results

The results indicated that MNR influences fetal bone development (femur and humerus) and fetal bone metabolic protein levels (C-terminal telopeptides of collagen I, CTx, in middle-gestation and N-terminal telopeptides of collagen I, NTx, in late-gestation), and maternal bone metabolites (low bone alkaline phosphatase, BALP, in middle-gestation and high BALP in late-gestation). The results show the importance of experimental conditions (ECs) encoding by mixing the information with the serum metabolic data. The best classification models obtained for femur weight (Fw) and length (FI), and humerus weight (Hw) are Support Vector Machines classifiers with the leave-one-out cross-validation accuracy of 1. The rest of the accuracies are 0.98, 0.946 and 0.696 for the diameter of femur (Fd), diameter and length of humerus (Hd, Hl), respectively. With the feature importance analysis, the moving averages mixed ECs are generally more important for the majority of the models. The moving average of parathyroid hormone (PTH) within nutritional conditions (MA-PTH-experim) is important for Fd, Hd and Hl prediction models but its removal for enhancing the Fw, Fl and Hw model performance. Further, using one feature models, it is possible to obtain even more accurate models compared with the feature importance analysis models. In conclusion, the machine learning is an efficient method to confirm the important role of PTH and BALP mixed with nutritional conditions for fetal bone growth performance of goats. All the Python scripts including results and comments are available into an open repository at https://gitlab.com/muntisa/goat-bones-machine-learning.

Biophysics and the nonlinear dynamics instigated by a special hormone

Calcitonin, a potent hypocalcemic hormone, plays a vital role in inhibiting osteoclastic activities and suppressing bone removal. The physiological characteristics of calcitonin have long been discussed, along a few recommending calcitonin as a vestigial hormone. The basis for this article is to discuss the role of low and high levels of calcitonin in normal and osteoprotic bone turnover. The effect of calcitonin on the receptor activator of nuclear factor kappa-ligand and osteoclasts has been demonstrated using numerical simulations. This behavior recommends that treatment of osteoporosis via calcitonin does not provide the required upshots. For effectiveness calcitonin must be advised along with a combined therapy like aspirin which agrees with the experimental results available in the literature.