An experimental comparative study of the biomechanical behavior of commonly used orthopedic implants for tibial plateau fractures was carried out. An artificial bone model Synbone1110 was used and a Schatzker V type tibial plateau fracture was created in vitro, then stabilized with three different implant types, classic L plate, Locking Plate System (PLS), and Hybrid External Fixator (HEF). The stiffness of the bone—implant assembly was assessed by means of mechanical testing using an automated testing machine. It was found that the classic L plate type internal implant has a significantly higher value of deformation then the other two implant types. In case of the other implant types, PLS had a better performance than HEF at low and medium values of the applied force. At high values of the applied forces, the difference between deformation values of the two types became gradually smaller. An Artificial Neural Network model was developed to predict the implant deformation as a function of the applied force and implant device type. To establish if a clear-cut distinction exists between mechanical performance of PLS and HEF, a Support Vector Machine classifier was employed. At high values of the applied force, the Support Vector Machine (SVM) classifier predicts that no statistically significant difference exists between the performance of PLS and HEF.

中文翻译：

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通过深度学习和支持向量机分类研究胫骨平台骨折的骨科植入物的生物力学特性

对常用的骨科植入物治疗胫骨平台骨折的生物力学行为进行了实验比较研究。使用了人造骨模型Synbone1110，并在体外创建了Schatzker V型胫骨平台骨折，然后用三种不同的植入物类型（经典L板，锁定板系统（PLS）和混合外固定器（HEF））进行稳定。骨-植入物组件的刚度通过使用自动化测试机的机械测试进行评估。发现经典的L板型内部植入物的变形值明显高于其他两种植入物类型。在其他植入物类型的情况下，PLS在中低施加力值下的性能优于HEF。在高施加力的情况下，两种类型的变形值之间的差异逐渐变小。开发了人工神经网络模型来预测植入物变形与施加力和植入装置类型的关系。为了确定PLS和HEF的机械性能之间是否存在明显区别，采用了支持向量机分类器。在高施加力值下，支持向量机（SVM）分类器预测PLS和HEF的性能之间不存在统计学上的显着差异。使用支持向量机分类器。在高施加力值下，支持向量机（SVM）分类器预测PLS和HEF的性能之间不存在统计学上的显着差异。使用支持向量机分类器。在高施加力值下，支持向量机（SVM）分类器预测PLS和HEF的性能之间不存在统计学上的显着差异。