Machrowska Anna, Szabelski Jakub, Karpiński Robert, Krakowski Przemysław, Jonak Józef, Jonak Kamil
Department of Machine Design and Mechatronics, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland.
Section of Biomedical Engineering, Department of Computerization and Production Robotization, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland.
Materials (Basel). 2020 Nov 28;13(23):5419. doi: 10.3390/ma13235419.
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.
本研究的目的是测试深度学习人工神经网络和统计建模在预测有缺陷骨水泥强度方面的实用性。这些缺陷与在制备阶段将诸如血液或盐水等外加剂作为污染物引入水泥中有关。由于深度学习在语音识别、生物信息处理和药物设计等诸多领域有广泛应用，因此研究了在何种程度上能够获取与骨水泥抗压强度预测相关的信息。开发和改进深度学习网络（DLN）算法以及统计建模以分析测试材料力学参数的变化，将有助于确定手术或水泥制备过程中相对于用于填充骨腔材料预期强度的可接受误差范围。因此，使用上述计算机方法可能在对手术效果进行初步定性评估中发挥重要作用，从而减少因未能维持所需力学参数而导致患者不满的误差。
