Design and application of ISSA-BP neural network model for predicting soft tissue relaxation force

• Autorzy: Yan Yongli , Ren Teng , Dind Li , Sun Tiansheng

Identyfikatory

DOI

10.37190/ABB-02529-2024-03

• Języki publikacji: EN

Abstrakty

EN

Accurate biomechanical modeling is crucial for enhancing the realism of virtual surgical training. This study addressed the computational cost and complexity associated with traditional viscoelastic models by incorporating neural network algorithms, thereby augmenting the predictive capability of soft tissue modeling. Methods: To address these challenges, the present study proposed a novel biomechanical modeling approach. The approach establishes a relaxation prediction model based on the backpropagation (BP) neural network and optimizes it using an enhanced sparrow search algorithm (ISSA). This hybrid method leverages the dynamic characteristics of forceps to predict the relaxation force of soft tissues more accurately. The ISSA optimizes the model by integrating chaos mapping, nonlinear inertia weight, and vertical–horizontal crossover strategy, which helps overcome the issue of local optima and boosts the predictive performance. Results: The experimental results demonstrated that the R2 values reached 0.9956 for the pig kidney and 0.9896 for the pig stomach, indicating the model’s exceptional precision in predicting relaxation forces. Conclusions: The relaxation force prediction model based on ISSA-BP neural network provides excellent predictive performance, offering a new and effective strategy for biomechanical modeling of soft tissues in virtual surgical systems.

Słowa kluczowe

EN

biomechanical modeling; viscoelasticity; relaxation force; neural network; sparrow search algorithm

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Acta of Bioengineering and Biomechanics
• Rocznik: 2024
• Tom: Vol. 26, no. 4
• Strony: 89--98
• Opis fizyczny: Bibliogr. 37 poz., rys., tab., wykr.

Twórcy

autor

Yan Yongli

• Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, China.

autor

Ren Teng

• School of Mechanical Engineering, Shenyang University of Technology, China.

autor

Dind Li

• School of Biological Science and Medical Engineering, Beihang University, China.

autor

Sun Tiansheng

• The Fourth Medical Center of China General Hospital of People’s Liberation Army, China.

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