Machine Learning Aided Photonic Diagnostic System for Minimally Invasive Optically Guided Surgery in the Hepatoduodenal Area.

Abdominal cancer is a widely prevalent group of tumours with a high level of mortality if diagnosed at a late stage. Although the cancer death rates have in general declined over the past few decades, the mortality from tumours in the hepatoduodenal area has significantly increased in recent years. The broader use of minimal access surgery (MAS) for diagnostics and treatment can significantly improve the survival rate and quality of life of patients after surgery. This work aims to develop and characterise an appropriate technical implementation for tissue endogenous fluorescence (TEF) and assess the efficiency of machine learning methods for the real-time diagnosis of tumours in the hepatoduodenal area. In this paper, we present the results of the machine learning approach applied to the optically guided MAS. We have elaborated tissue fluorescence approach with a fibre-optic probe to record the TEF and blood perfusion parameters during MAS in patients with cancers in the hepatoduodenal area. The measurements from the laser Doppler flowmetry (LDF) channel were used as a sensor of the tissue vitality to reduce variability in TEF data. Also, we evaluated how the blood perfusion oscillations are changed in the tumour tissue. The evaluated amplitudes of the cardiac (0.6-1.6 Hz) and respiratory (0.2-0.6 Hz) oscillations was significantly higher in intact tissues (