A Lightweight Algorithm for Classifying Ex Vivo Tissues Samples

TL;DR

This paper introduces a low-power, hardware-based neural network algorithm for classifying ex vivo tissue samples using bioimpedance analysis, suitable for integration into surgical probes for real-time tissue assessment.

Contribution

It presents a novel, hardware-implemented neural network algorithm optimized for bioimpedance tissue classification in surgical applications.

Findings

Achieved 90% accuracy on prostate tissue phantoms

Achieved 84% accuracy on bovine tissue model

Power consumption estimated at 39 mW

Abstract

In this paper, we present a novel algorithm for classifying ex vivo tissue that comprises multi-channel bioimpedance analysis and a hardware neural network. When implemented in a mixed-signal 180 nm CMOS process, the classifier has an estimated power budget of 39 mW and an area of 30 mm2. This means that the classifier can be integrated into the tip of a surgical margin assessment probe, for in vivo use during radical prostatectomy. We tested our classifier on digital phantoms of prostate tissue and also on an animal model of ex vivo bovine tissue. The classifier achieved an accuracy of 90% on the prostate tissue phantoms, and an accuracy of 84% on the animal model.