Predicting the polyethylene wear rate in pin-on-disc experiments in the context of prosthetic hip implants: Deriving a data-driven model using machine learning methods

Borjali, Alireza; Monson, Kenneth L.; Raeymaekers, Bart

doi:10.1016/j.triboint.2019.01.014

Cited by 51 publications

(29 citation statements)

References 101 publications

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“…We removed the annotations and anonymized the X‐rays by separating the embedded patient's information in the digital imaging and communications in medicine files from the X‐ray images prior to any analysis. We randomly split the X‐rays dataset into train, validation, and final test subsets with an 80:10:10 split ratio 19 . We maintained the split ratio among all the implant designs x‐rays to make sure that each design had representative X‐rays in the training, validation, and test subsets.…”

Section: Methodsmentioning

confidence: 99%

Detecting total hip replacement prosthesis design on plain radiographs using deep convolutional neural network

Borjali

Chen

Muratoglu

et al. 2020

Journal Orthopaedic Research

104

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Identifying the design of a failed implant is a key step in the preoperative planning of revision total joint arthroplasty. Manual identification of the implant design from radiographic images is time‐consuming and prone to error. Failure to identify the implant design preoperatively can lead to increased operating room time, more complex surgery, increased blood loss, increased bone loss, increased recovery time, and overall increased healthcare costs. In this study, we present a novel, fully automatic and interpretable approach to identify the design of total hip replacement (THR) implants from plain radiographs using deep convolutional neural network (CNN). CNN achieved 100% accuracy in the identification of three commonly used THR implant designs. Such CNN can be used to automatically identify the design of a failed THR implant preoperatively in just a few seconds, saving time and improving the identification accuracy. This can potentially improve patient outcomes, free practitioners' time, and reduce healthcare costs.

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Section: Methodsmentioning

confidence: 99%

Detecting total hip replacement prosthesis design on plain radiographs using deep convolutional neural network

Borjali

Chen

Muratoglu

et al. 2020

Journal Orthopaedic Research

104

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“…Anand et al [16] Zakaulla et al [92] Borjali et al [93] Xu et al [94] Gouarir et al [97] Tran et al [85] Slavkovic et al [99] Suresh et al [102] Optimization of friction parameters by using a force ANN. Predicted coefficient of friction and wear rate of polycarbonate-based composite by using ANN.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

“…Zakaulla et al [92] predicted coefficient of friction and wear rate of polycarbonate-based composite by using ANN and the main feature parameters were testing conditions and composition of the materials [92]. Borjali et al [93] employed different machine learning algorithms, such as gradient boosting, M5m CART, and linear regression, to predict the wear rate of polyethylene quantitively from pin-on-disc experiments [93]. Xu et al [94] developed three data-driven models, which are ANN, belief rule base (BRB) and evidential reasoning (ER) from the dataset that was generated from a wear-related problems in a diesel engine.…”

Section: Machine Learningmentioning

confidence: 99%

“…Thus, before the simulation, it should be critically analyzed which data set should be collected against known experimental conditions. After collecting, the dataset should be validated by identifying linear correlation coefficient among input and output data [37,93]. In addition, data should be analyzed by determining maximum and minimum data value, standard deviation, and stability.…”

Section: Data Acquisition and Cleaning Data Setmentioning

confidence: 99%

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Practice of Simulation and Life Cycle Assessment in Tribology—A Review

Kurdi

Alhazmi

et al. 2020

Materials

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To simulate today’s complex tribo-contact scenarios, a methodological breakdown of a complex design problem into simpler sub-problems is essential to achieve acceptable simulation outcomes. This also helps to manage iterative, hierarchical systems within given computational power. In this paper, the authors reviewed recent trends of simulation practices in tribology to model tribo-contact scenario and life cycle assessment (LCA) with the help of simulation. With the advancement of modern computers and computing power, increasing effort has been given towards simulation, which not only saves time and resources but also provides meaningful results. Having said that, like every other technique, simulation has some inherent limitations which need to be considered during practice. Keeping this in mind, the pros and cons of both physical experiments and simulation approaches are reviewed together with their interdependency and how one approach can benefit the other. Various simulation techniques are outlined with a focus on machine learning which will dominate simulation approaches in the future. In addition, simulation of tribo-contacts across different length scales and lubrication conditions is discussed in detail. An extension of the simulation approach, together with experimental data, can lead towards LCA of components which will provide us with a better understanding of the efficient usage of limited resources and conservation of both energy and resources.

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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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Predicting the polyethylene wear rate in pin-on-disc experiments in the context of prosthetic hip implants: Deriving a data-driven model using machine learning methods

Cited by 51 publications

References 101 publications

Detecting total hip replacement prosthesis design on plain radiographs using deep convolutional neural network

Detecting total hip replacement prosthesis design on plain radiographs using deep convolutional neural network

Practice of Simulation and Life Cycle Assessment in Tribology—A Review

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

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