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.
- Jufo-taso 2
!!ASJC Scopus subject areas
- Electrical and Electronic Engineering