Research on hip joint stress distribution algorithms based on deep learning

Abstract

Aiming at the problem of the stress distribution algorithm of hip cartilage,a deep learning model to replace the finite element analysis (FEA) was proposed.This deep learning model was divided into unsupervised learning module and supervised learning module.Firstly,an unsupervised learning module was adopted to encode the shape of hip cartilage and femur.Then the coding and decoding of stress distribution implement was implemented so that stress data can be combined with the neural network.Next a supervised learning module supervised by the stress data was used,and the model uses neural networks to learn a mapping relationship from the shape code of the hip cartilage and femur to the stress code of the stress distribution.Finally,a fitted deep learning model was obtained.This deep learning model can simulate the FEA method to a certain extent.But the mean absolute error and the normalized mean absolute error are still larger than that of the FEA method,so the FEA method cannot be completely replaced by our deep learning model.Meanwhile,the limitations of the deep learning model in the use of input features were studied,and a direction to improve the performance of the model was proposed.

Keywords

• hip cartilage;deep learning;stress distribution algorithm;FEA surrogate algorithm