The effect of energy modality on tissue identification from surgical smoke by differential ion mobility spectrometry

Abstract

Surgical smoke analysis offers a way to provide assisting information to the surgeon intraoperatively, which can be potentially used to assess cancer tumor margins in surgical oncology and alert the operator of accidental organ injuries caused by electrosurgical (ES) instruments. Surgical smoke content is affected by the energy instrument it is produced by. Classification of surgical smoke by differential ion mobility spectrometry (DMS) was evaluated with 6 porcine tissue types and 5 energy instruments. Instruments consisted of mono- and bipolar instruments, ultrasonic shears and a –blade. Machine learning was used to classify tissues by training binary classifier linear discriminant analysis (LDA) to distinguish a marked tissue class from the rest. The greatest binary classification accuracies were obtained with the monopolar instruments and the lowest with the bipolar instrument, 93.5 % and 77.5 % respectively. The analysis of surgical smoke with DMS is possible with a variety of energy instruments, however with varying performance. This implies that DMS based tissue identification is generalizable across different surgical instruments and surgical specialties.