Tissue Identification in a Porcine Model by Differential Ion Mobility Spectrometry Analysis of Surgical Smoke
Research output: Contribution to journal › Article › Scientific › peer-review
|Number of pages||10|
|Journal||Annals of Biomedical Engineering|
|Early online date||24 Apr 2018|
|Publication status||Published - Aug 2018|
|Publication type||A1 Journal article-refereed|
Electrosurgery is widely used in various surgical operations. When tissue is cut with high-frequency current, the cell contents at the incision area evaporate and together with water and possible soot particles, form surgical smoke. The smoke contains cell metabolites, and therefore, possible biomarkers for cancer or bacterial infection. Thus, the analysis of surgical smoke could be used in intraoperative medical diagnostics. We present a method that can be used to detect the characteristics of various tissue types by means of differential ion mobility spectrometry (DMS) analysis of surgical smoke. We used our method to test tissue identification with ten different porcine tissues. We classified the DMS responses with cross-validated linear discriminant analysis models. The classification accuracy in a measurement set with ten tissue types was 95%. The presented tissue identification by DMS analysis of surgical smoke is a proof-of-concept, which opens the possibility to research the method in diagnosing human tissues and diseases in the future.