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Prediction of Insertion Force of Bioinspired Needles using Machine Learning Algorithm
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Genre
Conference paper
Date
2019-10-16
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Mechanical Engineering
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DOI
http://dx.doi.org/10.34944/dspace/8153
Abstract
Simulation of surgical procedures provides a safe and potentially effective method for surgical training and robot-assisted surgery for pre- and intra-operative planning. Accurate modeling of the mechanical behavior of the interface of surgical needles and organs has been well recognized as a major challenge in the development of reliable surgical simulators. Prediction of needle insertion forces especially has been recognized as a major challenge during needle-tissue interactions. In this study, a machine-learning algorithm is proposed to estimate the insertion forces of bioinspired needles.
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Presented at the 2019 Biomedical Engineering Society (BMES) Annual Meeting, which took place October 16-19, 2019, in Philadelphia, PA.
Citation
Gidde, S. T. R., & Hutapea, P. (2019, October 16-19). Prediction of Insertion Force of Bioinspired Needles using Machine Learning Algorithm [Conference presentation abstract]. 2019 Biomedical Engineering Society (BMES) Annual Meeting, Philadephia, PA.
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