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 (p < 0.001) compared to the cancerous ones, while the myogenic (0.2-0.06 Hz) oscillation did not demonstrate any statistically significant difference. Our results demonstrate that a fibre-optic TEF probe accompanied with ML algorithms such as k-Nearest Neighbours or AdaBoost is highly promising for the real-time in situ differentiation between cancerous and healthy tissues by detecting the information about the tissue type that is encoded in the fluorescence spectrum. Also, we show that the detection can be supplemented and enhanced by parallel collection and classification of blood perfusion oscillations.

Publication DOI:
Divisions: Engineering & Applied Sciences > Aston Institute of Photonics Technology
Engineering & Applied Sciences
Additional Information: © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Uncontrolled Keywords: Blood perfusion,Endogenous fluorescence,Laser doppler flowmetry,Liver cancer,Machine learning,Minimally invasive interventions,Clinical Biochemistry
Full Text Link:
Related URLs: https://www.mdp ... -4418/10/11/873 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2020-10-27
Accepted Date: 2020-10-24
Authors: Zherebtsov, Evgeny
Zajnulina, Marina
Kandurova, Ksenia
Potapova, Elena
Dremin, Viktor (ORCID Profile 0000-0001-6974-3505)
Mamoshin, Andrian
Sokolovski, Sergei (ORCID Profile 0000-0001-7445-7204)
Dunaev, Andrey
Rafailov, Edik U. (ORCID Profile 0000-0002-4152-0120)



Version: Published Version

License: Creative Commons Attribution

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