Microtextured CoCrMo alloy for use in metal-on-polyethylene prosthetic joint bearings: Multi-directional wear and corrosion measurements

Langhorn, J.; Borjali, Alireza; Hippensteel, Elizabeth; Nelson, W.; Raeymaekers, Bart

doi:10.1016/j.triboint.2018.04.007

Cited by 36 publications

(18 citation statements)

References 37 publications

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“…Nevertheless, the effect of surface texturing was the most apparent in the case of conventional UHMWPE. Recent paper introduced by Langhorn et al [19] confirmed previous findings about metal-PE contact pair. The authors observed reduction of wear rate by more than 50% when textured metal surface was employed.…”

Section: Introductionsupporting

confidence: 63%

Running-in friction of hip joint replacements can be significantly reduced: The effect of surface-textured acetabular cup

et al. 2020

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Hip joint replacements represent the most effective way of treatment for patients suffering from joint diseases. Despite the rapid improvement of implant materials over the last few decades, limited longevity associated with wear-related complications persists as the main drawback. Therefore, improved tribological performance is required in order to extend the service life of replacements. The effect of surface texturing of ultrahigh molecular weight polyethylene (UHMWPE) acetabular cup was investigated in the present study. Unique tilling method was utilized for manufacturing the dimples with controlled diameter and depths on the contact surface of the cup. The experiments with four commercial femoral components and two model lubricants were realized. The main attention was paid to a coefficient of friction considering the differences between the original and the dimpled cups. Results showed remarkable lowering of friction, in general. Focusing on the simulated human synovial fluid, friction was reduced by 40% (alumina ceramic), 38.8% (zirconia toughened ceramic), 25.5% (metal), and 9.9% (oxinium). In addition, the dimples helped to keep the friction stable without fluctuations. To conclude, the paper brings a new insight into frictional behaviour of the hip replacements during running-in phase which is essential for overall implant lifespan. It is believed that proper surface texturing may rapidly improve the life quality of millions of patients and may lead to considerable financial savings.

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Section: Introductionsupporting

confidence: 63%

Running-in friction of hip joint replacements can be significantly reduced: The effect of surface-textured acetabular cup

et al. 2020

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“…In general, machine learning refers to a series of mathematical algorithms that enable the machine to ''learn'' the relationship between the input and output data without being explicitly told how to do so. 2 Machine learning itself can be further divided into more classical algorithms (e.g., support vector machine, decision tree, and neutral network) to extract knowledge from tabulated data sets, [2][3][4][5] and more recently developed ''deep learning'' algorithms (e.g., convolutional neural network [CNN]) to extract knowledge from imaging data sets. 6 Since orthopedic diagnosis and prognosis rely heavily on manual interpretation of medical images (X-ray, computed tomography scans, and magnetic resonance imaging), the application of AI in orthopedics has mainly focused on implementation of deep learning on these images.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning in Orthopedics: How Do We Build Trust in the Machine?

Borjali

Chen

Muratoglu

et al. 2020

Healthcare Transformation

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The paradigm of manual radiological assessment in orthopedics is at the point of significant disruption due to major breakthroughs in deep learning algorithms and computational power. Deep learning is a subset of the broader family of artificial intelligence methods that leverages artificial neural networks to assess imaging data sets. Deep learning has already been applied for automatic assessment of plain film radiographs with high degree of success in different orthopedic applications. However, a major limitation of deep learning is that it is a ''black-box'' algorithm, meaning its process and how it makes decisions are not easily interpretable. Interpretability in a medical setting is of utmost importance to build trust in the machine's outcome and move toward its meaningful integration into healthcare. In this article, we first briefly explain a deep learning algorithm and how it works at a high level. Next, we implement two different methods to enhance interpretability of a deep learning algorithm applied to an orthopedic application for detecting mechanical loosening of total hip replacement implants (1) on a high level using saliency maps and (2) on a lower level using activation maximization. By combining these two visualization methods, we can get a better understanding about deep learning function and shed light on its decision-making process to build more trust in the machine.

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“…Fundamental to all assembled systems, contact mechanics is integral to mechanical design. This is evident in many applications, such as: jointed structures (Brake, 2016), electrical contacts (Ghaednia et al, 2014), thermal contacts (Jackson et al, 2012), collision mechanics (Brake, 2012(Brake, , 2015Ghaednia et al, 2015;Gheadnia et al, 2015;Brake et al, 2017), continum mechanics (Golgoon et al, 2016;Yavari, 2017, 2018), biomechanics (Zhao et al, 2007;Borjali et al, 2017Borjali et al, , 2018Borjali et al, , 2019Langhorn et al, 2018;Mollaeian et al, 2018), turbines (Firrone and Zucca, 2011), additive manufacturing (Kardel et al, 2017;Pawlowski et al, 2017), bearings (Sadeghi et al, 2009), particle and powder interactions (Christoforou et al, 2013;Rathbone et al, 2015), and seals (Green and Etsion, 1985;Miller and Green, 2001) amongst other applications. Contact mechanics can be categorized into both single asperity and rough surface contact, where single asperity models are usually used in rough surface models.…”

Section: Introductionmentioning

confidence: 99%

Strain Hardening From Elastic-Perfectly Plastic to Perfectly Elastic Indentation Single Asperity Contact

et al. 2020

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Indentation measurements are a crucial technique for measuring mechanical properties. Although several contact models have been developed to relate force-displacement measurements with the mechanical properties, they all consider simplifying assumptions, such as no strain hardening, which significantly affects the predictions. In this study, the effect of bilinear strain hardening on the contact parameters for indentations is investigated. Simulations show that even 1% strain hardening causes significant changes in the contact parameters and contact profile. Pileup behavior is observed for elastic-perfectly plastic materials, while for strain hardening values greater than 6%, only sink-in (i.e., no pileup) is seen. These results are used to derive a new, predictive formulation to account for the bilinear strain hardening from elastic-perfectly plastic to purely elastic materials.

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Microtextured CoCrMo alloy for use in metal-on-polyethylene prosthetic joint bearings: Multi-directional wear and corrosion measurements

Cited by 36 publications

References 37 publications

Running-in friction of hip joint replacements can be significantly reduced: The effect of surface-textured acetabular cup

Running-in friction of hip joint replacements can be significantly reduced: The effect of surface-textured acetabular cup

Deep Learning in Orthopedics: How Do We Build Trust in the Machine?

Strain Hardening From Elastic-Perfectly Plastic to Perfectly Elastic Indentation Single Asperity Contact

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