Prediction Method of Flap Donor Closure Based on Neural Network
In view of the shortcomings of the finite element method to study the mechanism of flap incision closure,such as long time and strong expertise demand,this paper proposes a rapid prediction method of donor area closure stress and postoperative skin protrusion height based on neural network and finite element simulation.Firstly,a hyperelastic finite element model considering the longitudinal profile structure of the donor tissue was constructed,and mechanical simulation analysis was carried out for the incision closure of different geometric sizes and different tissue thicknesses,and a neural network dataset was established.A simulation test platform for notch closure was established,and digital image correlation(DIC)method was used to verify the reliability of the finite element model.Then,with the dataset of closed simulation results as the input,three models of BP(Back Propagation),RBF(Radial Basis Function)and EBF(Elliptic Basis Function)were trained and optimized,and the prediction model of cut closure was constructed.Finally,the prediction model was used to expand the sample data and the Sobol sensitivity analysis method was used to explore the influence of input parameters on the incision closure.The results showed that the EBF neural network has the best effect and could effectively predict the closure result of incision.The length of the short and long axis of incision have the greatest effect on the closure result,followed by the thickness of skin and the thickness of fat.At the same time,this paper analyzes the effect of different parameters on the closure effect,and provides a reference for the donor area suture surgery.
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