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A deep learning-based hybrid approach for the solution of multiphysics problems in electrosurgery.
Zhongqing Han1,2, Rahul2, Suvranu De1,2
1Department of Mechanical, Aerospace, and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, New York, 12180, USA.
Computer Methods in Applied Mechanics and Engineering
|September 1, 2020
Summary
This study introduces a hybrid deep learning and conventional solver approach to accelerate multiphysics modeling for electrosurgical dissection. The method significantly enhances computational efficiency while maintaining high accuracy in simulating soft tissue behavior.
Area of Science:
- Computational modeling
- Biomedical engineering
- Soft tissue mechanics
Background:
- Multiphysics modeling of electrosurgical dissection is computationally intensive.
- Accurate simulation of soft hydrated tissues requires advanced computational resources.
- Existing methods face challenges in balancing speed and precision.
Purpose of the Study:
- To develop a computationally efficient hybrid approach for multiphysics modeling of electrosurgical dissection.
- To accelerate complex simulations by integrating deep learning with conventional solvers.
- To overcome limitations of traditional methods and end-to-end deep learning.
Main Methods:
- A hybrid approach combining deep convolutional neural networks (CNNs) and finite element method (FEM) solvers.
- FEM with Krylov subspace solvers and deflation-based block preconditioners for electro-thermal problems.
- CNN model trained via supervised learning to predict mechanical deformation from micropore pressure.
Main Results:
- The hybrid approach demonstrates significantly higher computational efficiency compared to purely FEM-based or reduced-order model (PGD) approaches.
- Simulation accuracy is comparable to standard multiphysics solvers, validating the hybrid method.
- The approach mitigates the need for massive datasets typically required for end-to-end deep learning.
Conclusions:
- The proposed hybrid deep learning and FEM method offers a computationally efficient and accurate solution for multiphysics modeling in electrosurgical dissection.
- This approach effectively addresses the computational challenges associated with simulating soft hydrated tissues.
- It provides a viable alternative to existing methods, reducing computational burden without sacrificing accuracy.
