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.

Feedforward backpropagation artificial neural networks for predicting mechanical responses in complex nonlinear structures: A study on a long bone.

Feedforward backpropagation artificial neural networks (ANNs) have been increasingly employed in many engineering practices concerning materials modeling. Despite their extensive applications, how to achieve successfully trained ANNs is not thoroughly explained in the literature, nor are there lucid discussions to delineate influential parameters obtained from analyses. Long bones are composite materials possessing nonhomogeneous and anisotropic properties, and their mechanical responses exhibit dependency on numerous variables. Material complexity hinders researchers from arriving at a consensus in implementing an optimal constitutive model or encourages them to adopt a simple constitutive model including many simplifying assumptions. However, such exceptional features and engineering challenges make long bones materials worth investigating, enriching our comprehension of complex engineering structures using novel techniques where traditional methods may present limitations. This paper reports on the prediction of loading, displacement, load and displacement simultaneously, and strains using feedforward backpropagation ANNs trained with experimental recordings. The technique was used to find optimum network structures (architectures) that encompass the best prediction ability. To enhance predictions, the influence of several elements such as a network training algorithm, injecting noise to datasets prior to training, the level of injected noise which directly affects model fitting and regularization, and data normalization prior to training were investigated and discussed. Essential parameters influencing decision making in identifying well-trained and well-generalized ANNs were elaborated. A considerable emphasis in this study was placed on examining the generalization ability of the already trained and tested ANNs, thus guaranteeing unbiased models that avoided overfitting. Gaining favorable outcomes in this study required three years of performing experiments and data collection before establishing the networks. The subsequent training, testing, and determination of the generalization of more than 60,000 ANNs are promising and will assist researchers in comprehending mechanical responses of complicated engineering structures that exhibit peculiar nonlinear properties.

Optimization of the Mechanical Properties and the Cytocompatibility for the PMMA Nanocomposites Reinforced with the Hydroxyapatite Nanofibers and the Magnesium Phosphate Nanosheets.

Commercial poly methyl methacrylate (PMMA)-based cement is currently used in the field of orthopedics. However, it suffers from lack of bioactivity, mechanical weakness, and monomer toxicity. In this study, a PMMA-based cement nanocomposite reinforced with hydroxyapatite (HA) nanofibers and two-dimensional (2D) magnesium phosphate MgP nanosheets was synthesized and optimized in terms of mechanical property and cytocompatibility. The HA nanofibers and the MgP nanosheets were synthesized using a hydrothermal homogeneous precipitation method and tuning the crystallization of the sodium-magnesium-phosphate ternary system, respectively. Compressive strength and MTT assay tests were conducted to evaluate the mechanical property and the cytocompatibility of the PMMA-HA-MgP nanocomposites prepared at different ratios of HA and MgP. To optimize the developed nanocomposites, the standard response surface methodology (RSM) design known as the central composite design (CCD) was employed. Two regression models generated by CCD were analyzed and compared with the experimental results, and good agreement was observed. Statistical analysis revealed the significance of both factors, namely, the HA nanofibers and the MgP nanosheets, in improving the compressive strength and cell viability of the PMMA-MgP-HA nanocomposite. Finally, it was demonstrated that the HA nanofibers of 7.5% wt and the MgP nanosheets of 6.12% wt result in the PMMA-HA-MgP nanocomposite with the optimum compressive strength and cell viability.

What can artificial intelligence and machine learning tell us? A review of applications to equine biomechanical research.

Artificial intelligence (AI) and machine learning (ML) are fascinating interdisciplinary scientific domains where machines are provided with an approximation of human intelligence. The conjecture is that machines are able to learn from existing examples, and employ this accumulated knowledge to fulfil challenging tasks such as regression analysis, pattern classification, and prediction. The horse biomechanical models have been identified as an alternative tool to investigate the effects of mechanical loading and induced deformations on the tissues and structures in humans. Many reported investigations into bone fatigue, subchondral bone damage in the joints of both humans and animals, and identification of vital parameters responsible for retaining integrity of anatomical regions during normal activities in all species are heavily reliant on equine biomechanical research. Horse racing is a lucrative industry and injury prevention in expensive thoroughbreds has encouraged the implementation of various measurement techniques, which results in massive data generation. ML substantially accelerates analysis and interpretation of data and provides considerable advantages over traditional statistical tools historically adopted in biomechanical research. This paper provides the reader with: a brief introduction to AI, taxonomy and several types of ML algorithms, working principle of a feedforward artificial neural network (ANN), and, a detailed review of the applications of AI, ML, and ANN in equine biomechanical research (i.e. locomotory system function, gait analysis, joint and bone mechanics, and hoof function). 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.

Linear elastic and hyperelastic studies of equine hoof mechanical response at different hydration levels.

Most simulation studies on equine hoof biomechanics employed linear elastic (LE) material models. However, the equine hoof wall's stress-strain relationship is nonlinear and varies with hydration level. Therefore, it is essential to investigate the accuracy of the LE model compared to more advanced material models, such as hyperelastic (HE) or viscoelastic models. The current research investigated performances of LE and three HE models (Mooney-Rivlin, Neo-Hookean, and Marlow) in describing equine hoof's mechanical behavior using finite element (FE) analysis. In the first attempt, a rectangular tissue specimen was simulated using the previously published experimental data. The Marlow HE model predicted the hoof wall stress-strain curve more accurately than the LE, Mooney-Rivlin, and Neo-Hookean models. The LE model accuracy, compared with the experimental results, varied within the reported range of the strain. However, the Marlow HE model perfectly matched the experimental data for a wide range of strains. In the second attempt, the entire hoof, including nine associated tissues, was modeled from computed tomography (CT) scans of an equine forelimb, and analyzed at trotting and standing modes of locomotion. The effect of environmental humidity on the hoof wall material properties was incorporated at four hydration levels; 0%, 53%, 75%, and 100%. The simulation results of the LE and HE models indicated that the minimum principal strain distribution on the hoof wall remained under 2% for various hydration levels and gait conditions. The numerical results of the Marlow HE model demonstrated better agreement with published experimental data compared to the LE, Mooney-Rivlin, and Neo-Hookean models. Higher hydration levels significantly increased the strains - a potential explanation could be the fact that the higher hydration levels decreased stiffness of the hoof wall tissues and ultimately increased strains. Higher ground reaction forces increased the von Mises stress at various points in the hoof wall, especially in the quarter regions and close to the coronet, where cracks and fractures are found more often in the physiological conditions.

Process Optimization of In Situ Magnetic-Anisotropy Spark Plasma Sintering of M-Type-Based Barium Hexaferrite BaFe12O19.

This paper introduces a new spark plasma sintering technique that is able to order crystalline anisotropy by in-series/in situ DC electric coupled magnetic field. The process control parameters have been investigated on the production of anisotropic BaFe12O19 magnets based on resulted remanence (Mr). Sintering holding time (H.T.), cooling rate (C.R.), pressure (P), and sintering temperature (S.T.) are optimized by Taguchi with L9 orthogonal array (OA). The remanent magnetization of nanocrystalline BaFe12O19 in parallel (Mrǁ) and perpendicular (MrꞱ) to the applied magnetic field was regarded as a measure of performance. The Taguchi study calculated optimum process parameters, which significantly improved the sintering process based on the confirmation tests of BaFe12O19 anisotropy. The magnetic properties in terms of Mrǁ and MrꞱ were greatly affected by sintering temperature and pressure according to ANOVA results. In addition, regression models were developed for predicting the Mrǁ as well as MrꞱ respectively.