Calibrating surgical SmartForcepsTM using bootstrap and multilevel modeling techniques

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Date
2017
Authors
Azimaee, Parisa
Journal Title
Journal ISSN
Volume Title
Publisher
Canadian Medical and Biological Engineering Conference (CMBEC40)
Expert Review of Medical Devices
Abstract
Knowledge of forces, exerted on brain tissues during the performance of neurosurgeries, is critical for quality assurance, rehearsal, and training purposes. Quantifying the interaction forces has been made possible by developing SmartForceps, a bipolar forceps retrofitted by a set of strain gauges. The unknown values of implemented forces are estimated using voltages read from strain gauges. To this end, one needs to quantify the force-voltage relationship to estimate the interaction forces during microsurgery. In this thesis, we employed different probabilistic methodologies such as bootstrapping, weighted least squares regression, Bayesian regression and multi-level modeling in order to estimate the implemented force on tissue using voltages read from strain gauges. We obtain both point and interval estimates of the applied forces at the tool tips and calculate the precision associated with each point estimate. As a proof-of-concept, the proposed techniques were then employed to estimate unknown forces, and construct necessary confidence intervals using observed voltages in data sets.
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Keywords
Biostatistics
Citation
Azimaee, P., Jafari Jozani, M., Maddahi, Y., Zareinia, K. and Sutherland, G.R. (2017). Nonparametric Bootstrap Technique for Calibrating Surgical SmartForceps: Theory and Application.
Azimaee, P., Jafari Jozani, M., Maddahi, Y., Zareinia, K. and Sutherland, G.R. (2017). Calibration of an instrumented surgical forceps using bootstrap technique: A comparative study. In Canadian Medical and Biological Engineering Conference (CMBEC40) 2017 pp. 1-5.