Wear of orthopaedic implants and artificial joints

2012 ◽  
Author(s):  
Saverio Affatato
Keyword(s):  
Orthopaedic Implants ◽  
Artificial Joints

Assessment of Amorphous Carbon Coating for Artificial Joints Application

2005 ◽  
Vol 48 (2) ◽  
pp. 190-198 ◽  
Author(s):  
O. O. Ajayi ◽  
B. Shi ◽  
M. J. Soppet ◽  
A. Erdemir ◽  
H. Liang ◽  
...  
Keyword(s):  
Amorphous Carbon ◽  
Carbon Coating ◽  
Artificial Joints

The Role of Macrophages in the Biological Reaction to Wear Debris from Artificial Joints

2016 ◽  
Vol 26 (4) ◽  
pp. 303-309 ◽  
Author(s):  
Christophe Nich ◽  
Yuya Takakubo ◽  
Jukka Pajarinen ◽  
Jiri Gallo ◽  
Yrjo T. Konttinen ◽  
...  
Keyword(s):  
Wear Debris ◽  
Artificial Joints ◽  
Biological Reaction

scholarly journals Titanium-Nitride Coating of Orthopaedic Implants: A Review of the Literature

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Ruud P. van Hove ◽  
Inger N. Sierevelt ◽  
Barend J. van Royen ◽  
Peter A. Nolte
Keyword(s):  
Titanium Nitride ◽  
Polyethylene Wear ◽  
Ultrahigh Molecular Weight Polyethylene ◽  
Coating Process ◽  
Orthopaedic Implant ◽  
Cohesive Failure ◽  
Orthopaedic Implants ◽  
Tin Coating ◽  
Titanium Nitride Coating ◽  
English Medical Literature

Surfaces of medical implants can be enhanced with the favorable properties of titanium-nitride (TiN). In a review of English medical literature, the effects of TiN-coating on orthopaedic implant material in preclinical studies were identified and the influence of these effects on the clinical outcome of TiN-coated orthopaedic implants was explored. The TiN-coating has a positive effect on the biocompatibility and tribological properties of implant surfaces; however, there are several reports of third body wear due to delamination, increased ultrahigh molecular weight polyethylene wear, and cohesive failure of the TiN-coating. This might be due to the coating process. The TiN-coating process should be optimized and standardized for titanium alloy articulating surfaces. The clinical benefit of TiN-coating of CoCrMo knee implant surfaces should be further investigated.

scholarly journals A review of the bio-tribology of medical devices

Friction ◽  
2021 ◽  
Author(s):  
Xiaogang Zhang ◽  
Yali Zhang ◽  
Zhongmin Jin
Keyword(s):  
Service Life ◽  
Medical Devices ◽  
Tribological Behavior ◽  
Tribological Performance ◽  
Dental Restoration ◽  
Surgical Instruments ◽  
Cardiovascular Devices ◽  
Artificial Joints ◽  
Future Developments ◽  
Fixation Devices

AbstractNumerous medical devices have been applied for the treatment or alleviation of various diseases. Tribological issues widely exist in those medical devices and play vital roles in determining their performance and service life. In this review, the bio-tribological issues involved in commonly used medical devices are identified, including artificial joints, fracture fixation devices, skin-related devices, dental restoration devices, cardiovascular devices, and surgical instruments. The current understanding of the bio-tribological behavior and mechanism involved in those devices is summarized. Recent advances in the improvement of tribological properties are examined. Challenges and future developments for the prospective of bio-tribological performance are highlighted.

scholarly journals Morphological residual convolutional neural network (M-RCNN) for intelligent recognition of wear particles from artificial joints

Friction ◽  
2021 ◽  
Author(s):  
Xiaobin Hu ◽  
Jian Song ◽  
Zhenhua Liao ◽  
Yuhong Liu ◽  
Jian Gao ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
Support Vector ◽  
Wear Particles ◽  
Artificial Joints ◽  
Performance Deterioration ◽  
Overall Performance ◽  
Correct Category ◽  
Intelligent Recognition

AbstractFinding the correct category of wear particles is important to understand the tribological behavior. However, manual identification is tedious and time-consuming. We here propose an automatic morphological residual convolutional neural network (M-RCNN), exploiting the residual knowledge and morphological priors between various particle types. We also employ data augmentation to prevent performance deterioration caused by the extremely imbalanced problem of class distribution. Experimental results indicate that our morphological priors are distinguishable and beneficial to largely boosting overall performance. M-RCNN demonstrates a much higher accuracy (0.940) than the deep residual network (0.845) and support vector machine (0.821). This work provides an effective solution for automatically identifying wear particles and can be a powerful tool to further analyze the failure mechanisms of artificial joints.

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