Solid cancer tumors such as breast cancer or brain tumors are frequently removed using electric knifes. The removal generates surgical smoke that can be measured by so-called differential mobility spectrometry devices. Based on these measurements cancerous tissue can be distinguished from healthy tissue to ensure that the whole tumor and only the tumor is removed. The limitation for the current method is that it only works at some standard environmental conditions. Once temperature or humidity change, a reliable differentiation is impossible. This project at Tampere University will collect measurements from various tissue types at different temperatures and humidity levels to develop machine learning algorithms that allow to infer how measurements taken at any environmental settings would look like at standard conditions. This will allow faster and reliable identification of cancerous tissue during surgeries independent on the environmental conditions in the operating room.

2024

2028