Abstract
The introduction of robotic surgery has improved minimally invasive surgery, and now robotic surgery is used in several areas of surgical oncology. Several optical techniques can be used to discriminate cancer from healthy tissue based on their optical properties. These technologies can also be employed with a small fiber-optic probe during minimally invasive surgery; however, for acquiring reliable measurements, some optical techniques require the fiber-optic probe to be in direct contact with the tissue. The lack of tactile feedback in robotic surgery makes assessing tissue-probe contact suitable for optical contact measurements challenging for the surgeon. In this study, we investigated the use of single fiber reflectance (SFR) to determine tissue-probe contact adequately. A machine learning-based algorithm was developed to classify if direct tissue-probe contact was present during the measurement in an ex-vivo tissue setup. Using this classification algorithm, an average accuracy of 93.9% was achieved for assessing probe-tissue contact, suggesting that this technique can be utilized to assess tissue-probe contact in an in vivo clinical setting.