Numerical Study of the Biomechanical Behavior of a 3D Printed Polymer Esophageal Stent in the Esophagus by BP Neural Network Algorithm

Abstract

Esophageal disease is a common disorder of the digestive system that can severely affect the quality of life and prognosis of patients. Esophageal stenting is an effective treatment that has been widely used in clinical practice. However, esophageal stents of different types and parameters have varying adaptability and effectiveness for patients, and they need to be individually selected according to the patient's specific situation. The purpose of this study was to provide a reference for clinical doctors to choose suitable esophageal stents. We used 3D printing technology to fabricate esophageal stents with different ratios of thermoplastic polyurethane (TPU)/(Polyε-caprolactone) PCL polymer, and established an artificial neural network model that could predict the radial force of esophageal stents based on the content of TPU, PCL and print parameter. We selected three optimal ratios for mechanical performance tests and evaluated the biomechanical effects of different ratios of stents on esophageal implantation, swallowing, and stent migration processes through finite element numerical simulation and in vitro simulation tests. The results showed that different ratios of polymer stents had different mechanical properties, affecting the effectiveness of stent expansion treatment and the possibility of postoperative complications of stent implantation.