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

Guilin Wu1,2, Shenghua Huang1, Tingting Liu3, Zhuoni Yang3, Yuesong Wu2, Guihong Wei1, Peng Yu1,*, Qilin Zhang4, Jun Feng4, Bo Zeng5,*

1 College of Civil Engineering and Architecture, Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, Guangxi Key Laboratory of Disaster Prevention and Structural Safety, Scientific Research Center of Engineering Mechanics, Guangxi University, Nanning, 530004, China
2 Anaesthesiology Department of the 923th, The 923th Hospital of PLA Joint Logistic Support Force, Nanning, 530021, China
3 Biomedical Materials Engineering Research Center, Tsinghua Innovation Center in Dongguan, Dongguan, 523808, China
4 Research Centre, Guangxi Nanning Ruifeng Medical Devices Co., Ltd., Nanning, 530000, China
5 Department of Thoracic Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China

* Corresponding Authors: Peng Yu. Email: email; Bo Zeng. Email: email

(This article belongs to the Special Issue: Machine Learning Based Computational Mechanics)

Computer Modeling in Engineering & Sciences 2024, 138(3), 2709-2725. https://doi.org/10.32604/cmes.2023.031399

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)/(Poly-ε-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.

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APA Style
Wu, G., Huang, S., Liu, T., Yang, Z., Wu, Y. et al. (2024). Numerical study of the biomechanical behavior of a 3D printed polymer esophageal stent in the esophagus by BP neural network algorithm. Computer Modeling in Engineering & Sciences, 138(3), 2709-2725. https://doi.org/10.32604/cmes.2023.031399
Vancouver Style
Wu G, Huang S, Liu T, Yang Z, Wu Y, Wei G, et al. Numerical study of the biomechanical behavior of a 3D printed polymer esophageal stent in the esophagus by BP neural network algorithm. Comput Model Eng Sci. 2024;138(3):2709-2725 https://doi.org/10.32604/cmes.2023.031399
IEEE Style
G. Wu et al., "Numerical Study of the Biomechanical Behavior of a 3D Printed Polymer Esophageal Stent in the Esophagus by BP Neural Network Algorithm," Comput. Model. Eng. Sci., vol. 138, no. 3, pp. 2709-2725. 2024. https://doi.org/10.32604/cmes.2023.031399



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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