Use of Deep Learning Networks and Statistical Modeling to Predict Changes in Mechanical Parameters of Contaminated Bone Cements

Abstract

The purpose of the study was to test the usefulness of deep learning artificial neural networks and statistical modeling in predicting the strength of bone cements with defects. The defects are related to the introduction of admixtures, such as blood or saline, as contaminants into the cement at the preparation stage. Due to the wide range of applications of deep learning, among others in speech recognition, bioinformation processing, and medication design, the extent was checked to which it is possible to obtain information related to the prediction of the compressive strength of bone cements. Development and improvement of deep learning network (DLN) algorithms and statistical modeling in the analysis of changes in the mechanical parameters of the tested materials will enable determining an acceptable margin of error during surgery or cement preparation in relation to the expected strength of the material used to fill bone cavities. The use of the abovementioned computer methods may, therefore, play a significant role in the initial qualitative assessment of the effects of procedures and, thus, mitigation of errors resulting in failure to maintain the required mechanical parameters and patient dissatisfaction.

The Impact of Contaminating Poly (Methyl Methacrylate) (PMMA) Bone Cements on Their Compressive Strength

“…The overall results obtained in this study may be of use in carrying out further analyses leading to the mathematical modelling of the function of strength variations in relation to the contamination level, such as in [ 26 ], or in attempts to utilise such models for predicting strength values with further contamination using machine learning/neural networks [ 26 , 27 ]. It must also be kept in mind that temporary compressive strength and its changes, along with the increase in contamination levels, may not be directly transposed onto real-life conditions, i.e., the additional impact of time and seasoning/the functioning environment of such materials.…”

The Use of Neural Networks in the Analysis of Dual Adhesive Single Lap Joints Subjected to Uniaxial Tensile Test

“…The artificial neural network model was created based on analogies to biological counterparts. Neural networks are currently widely used in technical issues, among others [ 27 , 28 , 29 ]. They are a good solution for forecasting and regression problems.…”

Application of an Artificial Neural Network in the Modelling of Heat Curing Effects on the Strength of Adhesive Joints at Elevated Temperature with Imprecise Adhesive Mix Ratios

“…However, a large number of experimental tests is often infeasible, for example, because of the constraints of time and costs. One of the solutions can be the application of computer methods, such as the finite element method (FEM) [25][26][27][28][29][30], the boundary element method (BEM) [31][32][33][34], predictive modelling [35][36][37][38][39], and data analytics [40][41][42][43][44][45]. Mathematical modelling with a modest dataset acquired may help to determine the relationships between the individual parameters and mechanical properties, to identify the most promising direction of research, to reduce the number of physical tests, and to markedly reduce the time and costs of research.…”