Modeling Electromagnetic Navigation Systems for Medical Applications using Random Forests and Artificial Neural Networks
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Date
2020
Publication Type
Conference Paper
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yes
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Abstract
Electromagnetic Navigation Systems (eMNS) can be used to control a variety of multiscale devices within the human body for remote surgery. Accurate modeling of the magnetic fields generated by the electromagnets of an eMNS is crucial for the precise control of these devices. Existing methods assume a linear behavior of these systems, leading to significant modeling errors within nonlinear regions exhibited at higher magnetic fields, preventing these systems from operating at full capacity. In this paper, we use a random forest (RF) and an artificial neural network (ANN) to model the nonlinear behavior of the magnetic fields generated by an eMNS. Both machine learning methods outperformed the state-of-the-art linear multipole electromagnet model (MPEM). The RF and the ANN model reduced the root mean squared error (RMSE) of the MPEM when predicting the field magnitude by approximately 40% and 87%, respectively, over the entire current range of the eMNS. At high current regions, especially between 30 and 35 A, the field-magnitude RMSE improvement of the ANN model over the MPEM was 37 mT, equivalent to 90% error reduction. This study demonstrates the feasibility of using machine learning to model an eMNS for medical applications, and its ability to account for complex nonlinear behavior at high currents. The use of machine learning thus shows promise in developing accurate field predicting models, and ultimately improving surgical procedures that use magnetic navigation. © 2020 IEEE.
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published
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Book title
2020 IEEE International Conference on Robotics and Automation (ICRA)
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Pages / Article No.
9251 - 9256
Publisher
IEEE
Event
IEEE International Conference on Robotics and Automation (ICRA 2020)
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Organisational unit
03627 - Nelson, Bradley J. / Nelson, Bradley J.
Notes
Due to the Coronavirus (COVID-19) the conference was conducted virtually.
Funding
165564 - Soft Magnetic Robots: Modeling, Design and Control of Magnetically Guided Continuum Manipulators (SNF)
743217 - Soft Micro Robotics (EC)
743217 - Soft Micro Robotics (EC)
Related publications and datasets
Is new version of: https://doi.org/10.3929/ethz-b-000395938