Nonparametric bootstrap technique for calibrating surgical SmartForceps: theory and application

Area of Science:

• Neurosurgery
• Biomedical Engineering
• Medical Instrumentation

Background:

• Accurate quantification of forces exerted on brain tissue during neurosurgical procedures is crucial for quality assurance, rehearsal, and training.
• SmartForceps, equipped with strain gauges, estimate interaction forces via voltage readings, necessitating a precise force-voltage relationship.
• Previous methods often lack precision in force estimation due to reliance on deterministic properties of strain gauges.

Purpose of the Study:

• To develop and validate a probabilistic methodology for precise estimation of tool-tissue interaction forces in neurosurgery.
• To quantify the force-voltage relationship using a nonparametric Bootstrap approach, providing both point and interval estimates.
• To assess the accuracy and precision of the Bootstrap technique compared to traditional methods.

Main Methods:

• A nonparametric Bootstrap approach was employed to analyze voltage data from strain gauges in SmartForceps.
• The technique was used to estimate unknown forces and construct confidence intervals for applied forces at the tool tips.
• The methodology was validated using data from surgical tasks performed on a cadaveric brain model.

Main Results:

• The Bootstrap technique demonstrated capability in estimating tool-tissue interaction forces with acceptable accuracy.
• Confidence intervals were successfully constructed, providing a measure of precision for each force estimate.
• Results showed comparable or improved accuracy compared to linear regression under normality assumptions.

Conclusions:

• The probabilistic Bootstrap approach offers a precise and accurate method for estimating neurosurgical tool-tissue interaction forces.
• This technique enhances the reliability of force measurements, benefiting surgical training, rehearsal, and quality assurance.
• The study highlights the potential of advanced statistical methods in improving microsurgical instrumentation and data analysis.