Alex Tinggaard Årsvold, Andreas Sørensen Zeltner, Zhuoqi Cheng, K. Schwaner, Pernille Tine Jensen, T. Savarimuthu
{"title":"Lymph Node Detection Using Robot Assisted Electrical Impedance Scanning and an Artificial Neural Network","authors":"Alex Tinggaard Årsvold, Andreas Sørensen Zeltner, Zhuoqi Cheng, K. Schwaner, Pernille Tine Jensen, T. Savarimuthu","doi":"10.1109/ismr48346.2021.9661502","DOIUrl":null,"url":null,"abstract":"Lymphadenectomy is frequently performed as a surgical treatment for cancer. Lymph nodes grow inside fat and have similar color as fat, making them difficult to detect. In Robotic Assisted Minimally Invasive Surgery (RAMIS), it can be even more challenging due to the lack of haptic feedback. This study proposes a novel method to measure the electrical property of a target tissue site and determine whether a lymph node is present underneath through an Artificial Neural Network classifier. The proposed system and method are built, analyzed, and evaluated based on simulation and ex vivo tissue phantom experiments. The experimental results show a very high accuracy (93.49%) in detecting a lymph node that is embedded deep inside fat. Given the promising results and the portability of the proposed system, we believe it has great potential to improve the quality of related surgical procedures.","PeriodicalId":405817,"journal":{"name":"2021 International Symposium on Medical Robotics (ISMR)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Symposium on Medical Robotics (ISMR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ismr48346.2021.9661502","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Lymphadenectomy is frequently performed as a surgical treatment for cancer. Lymph nodes grow inside fat and have similar color as fat, making them difficult to detect. In Robotic Assisted Minimally Invasive Surgery (RAMIS), it can be even more challenging due to the lack of haptic feedback. This study proposes a novel method to measure the electrical property of a target tissue site and determine whether a lymph node is present underneath through an Artificial Neural Network classifier. The proposed system and method are built, analyzed, and evaluated based on simulation and ex vivo tissue phantom experiments. The experimental results show a very high accuracy (93.49%) in detecting a lymph node that is embedded deep inside fat. Given the promising results and the portability of the proposed system, we believe it has great potential to improve the quality of related surgical procedures.
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