Nonparametric bootstrap technique for calibrating surgical SmartForceps: theory and application

ABSTRACT

Knowledge of forces, exerted on the brain tissue during the performance of neurosurgical tasks, is critical for quality assurance, case 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 forces are estimated using voltages read from strain gauges. We therefore need to quantify the force-voltage relationship to estimate the interaction forces during microsurgery. This problem has been addressed in the literature by following the physical and deterministic properties of the force-sensing strain gauges without obtaining the precision associated with each estimate. In this paper, we employ a probabilistic methodology by using a nonparametric Bootstrap approach to obtain both point and interval estimates of the applied forces at the tool tips, while the precision associated with each estimate is provided. To show proof-of-concept, the Bootstrap technique is employed to estimate unknown forces, and construct necessary confidence intervals using observed voltages in data sets that are measured from the performance of surgical tasks on a cadaveric brain. Results indicate that the Bootstrap technique is capable of estimating tool-tissue interaction forces with acceptable level of accuracy compared to the linear regression technique under the normality assumption.

KEYWORDS:

• Calibration
• bootstrap technique
• SmartForceps
• force interval
• neurosurgery
• medical forceps
• tool-tissue interaction force
• surgical training

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Ethical Approval

The experiments were performed with approval from the Conjoint Health Research Ethics Board (CHREB) of the University of Calgary. The cadaver head was obtained through the Body Donation Program, Department of Anatomy at the University of Calgary, Alberta, Canada.

Notes

1. Bias in Bootstrap method is the difference between expectation of Bootstrap’s estimates and the true force value.

2. Bias in Naïve method is defined as the difference between expectation of estimation based on the Naïve method and the true force.

3. Bias in extension of the Eisenhart’s method is the difference between expectation of estimation based on the extension of the Eisenhart’s method and the true value of force.

Additional information

Funding

The work was supported by grants from Canada Foundation for Innovation, Western Economic Diversification, and Alberta Advanced Education and Technology. P. Azimaee and M. J. Jozani acknowledge the research support of the Natural Sciences and Engineering Research Council of Canada. The authors would like to thank the Killam Trusts, whose generous support for Killam Laureate Y. Maddahi allowed his contribution to this work.