Development of a tissue discrimination electrode embedded surgical needle using vibro-tactile feedback derived from electric impedance spectroscopy.

Some tumours may not be detected by ultrasound during biopsy, but recent evidence has shown that different tissues can be discerned by electric impedance. This paper explores the application of vibrotactile feedback in an electrode embedded needle to help classify tissue during fine-needle aspiration biopsy from bioimpedance measurements. The process uses electric impedance spectroscopy from 10 Hz to 349 kHz to fit the double-dispersion Cole model through the Newton-Raphson algorithm. A Naive Bayes classifier is then used on the equivalent circuit parameters to estimate the tissue at the needle tip. The method is validated through a series of experiments and user trials. The results show that the vibrotactile feedback is able to help the operator in determining the tissue the needle is in, suggesting that this may prove to be a useful supplement to the standard procedure used today. Graphical Abstract This paper explores the application of vibrotactile feedback for an electrode embedded needle to help classify tissue from electric impedance measurements. The data is fit to an equivalent circuit using Newton- Raphon's method. A Naive Bayes classifier is trained and later used in an experiment to estimate the tissue at the needle tip and provide an assigned vibrotacticle feedback to the user.