Real Time Tissue Identification from Diathermy Smoke by Differential Mobility Spectrometry

Anton Kontunen, Markus Karjalainen, Anna Anttalainen, Osmo Anttalainen, Mikko Koskenranta, Antti Vehkaoja, Niku Oksala, Antti Roine

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

Current methods for intraoperative surgical margin assessment are inadequate in terms of diagnostic accuracy, ease-of-use, and speed of analysis. Molecular analysis of tissues could potentially overcome these issues. A system based on differential ion mobility spectrometry (DMS) analysis of surgical smoke has been proposed as one potential method, but to date, it has been able to function in a relatively slow and heavily controlled manner that is inadequate for clinical use. In this study, we present an integrated sensor system that can measure a surgical smoke sample in seconds and relay the information of the tissue type to the user in near real time in simulated surgical use. The system was validated by operating porcine adipose tissue and muscle tissue. The differentiation of these tissues based on their surgical smoke profile with a cross-validated linear discriminant analysis model produced a classification accuracy of 93.1% (N = 1059). The measurements were also classified with a convolutional neural network model, resulting in a classification accuracy of 93.2%. These results indicate that the DMS-based smoke analysis system is capable of rapid tissue identification from surgical smoke produced in freehand surgery.

Original languageEnglish
JournalIEEE Sensors Journal
Early online date30 Jul 2020
DOIs
Publication statusPublished - 2021
Publication typeA1 Journal article-refereed

Keywords

  • Biomedical engineering
  • biomedical measurement
  • differential mobility spectrometry
  • particle filter
  • supervised learning
  • surgical instruments

Publication forum classification

  • Publication forum level 2

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering

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