Characterization of interfacial failure in cemented total joint replacements via self-sensing bone cement, electrical impedance tomography, and machine learning

2021 ◽  
Author(s):  
Hamid Ghaednia ◽  
Crystal Owens ◽  
Lily E. Keiderling ◽  
John Hart ◽  
Kartik Varadarajan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bone Cement ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Interfacial Failure ◽  
Impedance Tomography ◽  
Joint Replacements ◽  
Total Joint Replacements

scholarly journals Classification of Electrical Impedance Tomography Data Using Machine Learning

2021 ◽  
Author(s):  
Diogo Pessoa ◽  
Bruno Machado Rocha ◽  
Grigorios-Aris Cheimariotis ◽  
Kostas Haris ◽  
Claas Strodthoff ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Impedance Tomography ◽  
Tomography Data ◽  
Electrical Impedance Tomography Data

scholarly journals Comparison of Selected Machine Learning Algorithms for Industrial Electrical Tomography

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1521 ◽  
Author(s):  
Tomasz Rymarczyk ◽  
Grzegorz Kłosowski ◽  
Edward Kozłowski ◽  
Paweł Tchórzewski
Keyword(s):  
Machine Learning ◽  
Electrical Impedance Tomography ◽  
Newton Method ◽  
Electrical Impedance ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Electrical Tomography ◽  
Learning Methods ◽  
Impedance Tomography ◽  
Machine Learning Methods

The main goal of this work was to compare the selected machine learning methods with the classic deterministic method in the industrial field of electrical impedance tomography. The research focused on the development and comparison of algorithms and models for the analysis and reconstruction of data using electrical tomography. The novelty was the use of original machine learning algorithms. Their characteristic feature is the use of many separately trained subsystems, each of which generates a single pixel of the output image. Artificial Neural Network (ANN), LARS and Elastic net methods were used to solve the inverse problem. These algorithms have been modified by a corresponding increase in equations (multiply) for electrical impedance tomography using the finite element method grid. The Gauss-Newton method was used as a reference to machine learning methods. The algorithms were trained using learning data obtained through computer simulation based on real models. The results of the experiments showed that in the considered cases the best quality of reconstructions was achieved by ANN. At the same time, ANN was the slowest in terms of both the training process and the speed of image generation. Other machine learning methods were comparable with the deterministic Gauss-Newton method and with each other.

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