Development of finite element simulation and machine learning models relating to Electrical Impedance Spectroscopy

开发与电阻抗频谱相关的有限元仿真和机器学习模型，以便在甲状腺手术中区分不同的组织类型。电阻抗频谱(EIS)是一种通过在一定交流频率范围内测量的被动电特性来识别不同组织类型的方法。它已经被成功地用于识别早期宫颈癌(谢菲尔德大学Zilico)，早期数据表明，它可以作为一种工具，通过区分视觉上相似的组织类型来指导甲状腺切除术的手术干预。本项目旨在使用在手术过程中收集的现有数据集的机器学习和有限元模拟技术来回答以下问题：i)阻抗谱的哪些特征能够最好地区分组织类型？ii)产生这些特征的组织的特征是什么？这将最终支持用于EIS引导手术的商业引导仪器的设计。

Development of finite element simulation and machine learning models relating to Electrical Impedance Spectroscopy in order to discriminate between different tissue types during thyroid surgery.Electrical Impedance Spectroscopy (EIS) is a method for identifying different tissue types by its passive electrical characteristics measured over a range of AC frequencies. It has been successfully used to identify early cancers in the cervix (Zedscan, University of Sheffield, Zilico), and early data suggests that it may be applicable as a tool to guide surgical intervention in thyroidectomy by discriminating between visually similar tissue types.This project aims to use both machine learning on an existing data set, collected during surgery and finite element simulation techniques to answer the following questions:i) Which characteristics of the impedance spectrum give best discrimination between the tissue types?ii) What are the characteristics of the tissue that give rise to these features? This will ultimately support the design of a commercially guided instrument for EIS-guided surgery.