  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.
