Artificial intelligence and machine learning as a viable solution for hip implant failure diagnosis-Review of literature and in vitro case study.

The digital health industry is experiencing fast-paced research which can provide digital care programs and technologies to enhance the competence of healthcare delivery. Orthopedic literature also confirms the applicability of artificial intelligence (AI) and machine learning (ML) models to medical diagnosis and clinical decision-making. However, implant monitoring after primary surgery often happens with a wellness visit or when a patient complains about it. Neglecting implant design and other technical errors in this scenario, unmonitored circumstances, and lack of post-surgery monitoring may ultimately lead to the implant system's failure and leave us with the only option of high-risk revision surgery. Preventive maintenance seems to be a good choice to identify the onset of an irreversible prosthesis failure. Considering all these aspects for hip implant monitoring, this paper explores existing studies linking ML models and intelligent systems for hip implant diagnosis. This paper explores the feasibility of an alternative continuous monitoring technique for post-surgery implant monitoring backed by an in vitro ML case study. Tribocorrosion and acoustic emission (AE) data are considered based on their efficacy in determining irreversible alteration of implant material to prevent total failures. This study also facilitates the relevance of developing an artificially intelligent implant monitoring methodology that can function with daily patient activities and how it can influence the digital orthopedic diagnosis. AI-based non-invasive hip implant monitoring system enabling point-of-care testing.

Total hip replacement monitoring: numerical models for the acoustic emission technique.

Any mechanical instability associated with total hip replacement (THR) excites elastic waves with different frequencies and propagates through the surrounding biological layers. Using the acoustic emission (AE) technique as a THR monitoring tool provides valuable information on structural degradations associated with these implants. However, several factors can compromise the reliability of the signals detected by AE sensors, such as attenuation of the detected signal due to the presence of biological layers in the human body between prosthesis (THR) and AE sensor. The main objective of this study is to develop a numerical model of THR that evaluates the impact of biological layer thicknesses on AE signal propagation. Adipose tissue thickness, which varies the most between patients, was modeled at two different thicknesses 40 mm and 70 mm, while the muscle and skin thicknesses were kept to a constant value. The proposed models were tested at different micromotions of 2 µm, 15-20 µm at modular junctions, and different frequencies of 10-60 kHz. Attenuation of signal is observed to be more with an increase in the selected boundary conditions along with an increase in distance the signals propagate through. Thereby, the numerical observations drawn on each interface helped to simulate the effect of tissue thicknesses and their impact on the attenuation of elastic wave propagation to the AE receiver sensor.

Finite element modeling of passive material influence on the deformation and force output of skeletal muscle.

The pattern of deformation of different structural components of a muscle-tendon complex when it is activated provides important information about the internal mechanics of the muscle. Recent experimental observations of deformations in contracting muscle have presented inconsistencies with current widely held assumption about muscle behavior. These include negative strain in aponeuroses, non-uniform strain changes in sarcomeres, even of individual muscle fibers and evidence that muscle fiber cross sectional deformations are asymmetrical suggesting a need to readjust current models of contracting muscle. We report here our use of finite element modeling techniques to simulate a simple muscle-tendon complex and investigate the influence of passive intramuscular material properties upon the deformation patterns under isometric and shortening conditions. While phenomenological force-displacement relationships described the muscle fiber properties, the material properties of the passive matrix were varied to simulate a hydrostatic model, compliant and stiff isotropically hyperelastic models and an anisotropic elastic model. The numerical results demonstrate that passive elastic material properties significantly influence the magnitude, heterogeneity and distribution pattern of many measures of deformation in a contracting muscle. Measures included aponeurosis strain, aponeurosis separation, muscle fiber strain and fiber cross-sectional deformation. The force output of our simulations was strongly influenced by passive material properties, changing by as much as ~80% under some conditions. The maximum output was accomplished by introducing anisotropy along axes which were not strained significantly during a muscle length change, suggesting that correct costamere orientation may be a critical factor in the optimal muscle function. Such a model not only fits known physiological data, but also maintains the relatively constant aponeurosis separation observed during in vivo muscle contractions and is easily extrapolated from our plane-strain conditions into a three-dimensional structure. Such modeling approaches have the potential of explaining the reduction of force output consequent to changes in material properties of intramuscular materials arising in the diseased state such as in genetic disorders.

Finite element modeling reveals complex strain mechanics in the aponeuroses of contracting skeletal muscle.

A finite element model was used to investigate the counter-intuitive experimental observation that some regions of the aponeuroses of a loaded and contracting muscle may shorten rather than undergo an expected lengthening. The model confirms the experimental findings and suggests that pennation angle plays a significant role in determining whether regions of the aponeuroses stretch or shorten. A smaller pennation angles (25 degrees ) was accompanied by aponeurosis lengthening whereas a larger pennation angle (47 degrees ) was accompanied by mixed strain effects depending upon location along the length of the aponeurosis. This can be explained by the Poisson effect during muscle contraction and a Mohr's circle analogy. Constant volume constraint requires that fiber cross sectional dimensions increase when a fiber shortens. The opposing influences of these two strains upon the aponeurosis combine in proportion to the pennation angle. Lower pennation angles emphasize the influence of fiber shortening upon the aponeurosis and thus favor aponeurosis compression, whereas higher pennation angles increase the influence of cross sectional changes and therefore favor aponeurosis stretch. The distance separating the aponeuroses was also found to depend upon pennation angle during simulated contractions. Smaller pennation angles favored increased aponeurosis separation larger pennation angles favored decreased separation. These findings caution that measures of the mechanical properties of aponeuroses in intact muscle may be affected by contributions from adjacent muscle fibers and that the influence of muscle fibers on aponeurosis strain will depend upon the fiber pennation angle.