ArticleMaterials (Basel, Switzerland)2020
Use of Deep Learning Networks and Statistical Modeling to Predict Changes in Mechanical Parameters of Contaminated Bone Cements.
Article in Materials (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Mechanical Properties and Functional Assessment of PMMA Bone Cements Modified with Glassy Carbon.Journal of functional biomaterials · 2025Article
- Cemented vs. Cementless Fixation in Primary Knee Replacement: A Narrative Review.Materials (Basel, Switzerland) · 2024Review
- Application of 3D Printing in Bone Grafts.Cells · 2023Review
- Adhesive Joint Degradation Due to Hardener-to-Epoxy Ratio Inaccuracy under Varying Curing and Thermal Operating Conditions.Materials (Basel, Switzerland) · 2022Article
- Computationally Efficient Concept of Representative Directions for Anisotropic Fibrous Materials.Polymers · 2022Article
- Analysis of the Effect of Component Ratio Imbalances on Selected Mechanical Properties of Seasoned, Medium Viscosity Bone Cements.Materials (Basel, Switzerland) · 2022Article
- Evaluation of the Effect of Selected Physiological Fluid Contaminants on the Mechanical Properties of Selected Medium-Viscosity PMMA Bone Cements.Materials (Basel, Switzerland) · 2022Article
- Prediction of Selected Mechanical Properties of Polymer Composites with Alumina Modifiers.Materials (Basel, Switzerland) · 2022Article
- 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.Materials (Basel, Switzerland) · 2022Article
- Traditional Artificial Neural Networks Versus Deep Learning in Optimization of Material Aspects of 3D Printing.Materials (Basel, Switzerland) · 2021Article
- The Impact of Contaminating Poly (Methyl Methacrylate) (PMMA) Bone Cements on Their Compressive Strength.Materials (Basel, Switzerland) · 2021Article
- The Use of Neural Networks in the Analysis of Dual Adhesive Single Lap Joints Subjected to Uniaxial Tensile Test.Materials (Basel, Switzerland) · 2021Article
- Effect of Physiological Saline Solution Contamination on Selected Mechanical Properties of Seasoned Acrylic Bone Cements of Medium and High Viscosity.Materials (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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.
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