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Simulation of Tissue Insertion Force of Surgical Needles using Neural Network Model
Verissimo, Tolulope Chen, Nuo
Verissimo, Tolulope
Chen, Nuo
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Genre
Poster (Research)
Date
2018-03-29
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Mechanical Engineering
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DOI
http://dx.doi.org/10.34944/dspace/8166
Abstract
Needles are commonly used to reach target locations inside of the body for various medical interventions. The effectiveness of these procedures depends on the accuracy with which the needle tips reach the targets. One of the major issues in needle steering is the insertion (penetration) force. The insertion force causes tissue damage as well as tissue deformation. Simulation of surgical procedures provides an effective method for a robot-assisted surgery for pre- and intra-operative planning. Accurate modeling of the mechanical behavior on the interface of surgical needles and organs/tissues, specifically the insertion force, has been well recognized as a major challenge. This study investigates feasibility of predicting the insertion force of surgical needles based on experimental data using Neural Network modeling. Simulation of the proposed neural network model is performed using Keras Python Deep Learning Library with TensorFlow as a backend. The insertion forces of needles with different bevel-tip angles in gel tissue phantom are measured using an automated needle insertion test setup. Through the experiments, input-output datasets are generated where the inputs are defined as the bevel-tip angle and the stiffness of the gel phantom, and the output is defined as the insertion force. A properly trained neural network then maps the input data to the output data and the input-output dataset is supplied to train the neural network model. The performance of the model is then evaluated using different and unseen input-output dataset. This work shows that the proposed neural network model accurately predicts the insertion force.
Description
Presented at the 44th Annual Northeast Bioengineering Conference (NEBEC), which took place March 28-30, 2018, in Philadelphia, PA.
Citation
Gidde, S. T. R., Verissimo, T., Chen, N. I., Loh, B-G., & Hutapea, P. (2019, March 28-30). Simulation of Tissue Insertion Force of Surgical Needles using Neural Network Model. Poster presented at the 44th Annual Northeast Bioengineering Conference (NEBEC), Philadelphia, PA.
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