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.

翻译：本文提出了一种用于离体组织分类的新型算法，该算法结合了多通道生物阻抗分析和硬件神经网络。当采用混合信号180纳米CMOS工艺实现时，该分类器的预估功耗为39毫瓦，面积为30平方毫米。这意味着该分类器可以集成到手术切缘评估探头的尖端，用于根治性前列腺切除术期间的体内应用。我们在前列腺组织数字模型以及离体牛组织的动物模型上测试了我们的分类器。该分类器在前列腺组织模型上达到了90%的准确率，在动物模型上达到了84%的准确率。