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Open AccessArticle

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

1
Research and Development Center of Biomedical Photonics, Orel State University, 302026 Orel, Russia
2
Faculty of Information Technology and Electrical Engineering, University of Oulu, Optoelectronics and Measurement Techniques Unit, 90570 Oulu, Finland
3
Aston Institute of Photonic Technologies, Aston University, Birmingham B4 7ET, UK
4
Department of X-ray Surgical Methods of Diagnosis and Treatment, Orel Regional Clinical Hospital, 302028 Orel, Russia
*
Author to whom correspondence should be addressed.
Diagnostics 2020, 10(11), 873; https://doi.org/10.3390/diagnostics10110873
Received: 28 August 2020 / Revised: 19 October 2020 / Accepted: 24 October 2020 / Published: 27 October 2020
(This article belongs to the Special Issue Optical Diagnostics in Human Diseases)
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. View Full-Text
Keywords: liver cancer; endogenous fluorescence; laser Doppler flowmetry; blood perfusion; minimally invasive interventions; machine learning liver cancer; endogenous fluorescence; laser Doppler flowmetry; blood perfusion; minimally invasive interventions; machine learning
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MDPI and ACS Style

Zherebtsov, E.; Zajnulina, M.; Kandurova, K.; Potapova, E.; Dremin, V.; Mamoshin, A.; Sokolovski, S.; Dunaev, A.; Rafailov, E.U. Machine Learning Aided Photonic Diagnostic System for Minimally Invasive Optically Guided Surgery in the Hepatoduodenal Area. Diagnostics 2020, 10, 873.

AMA Style

Zherebtsov E, Zajnulina M, Kandurova K, Potapova E, Dremin V, Mamoshin A, Sokolovski S, Dunaev A, Rafailov EU. Machine Learning Aided Photonic Diagnostic System for Minimally Invasive Optically Guided Surgery in the Hepatoduodenal Area. Diagnostics. 2020; 10(11):873.

Chicago/Turabian Style

Zherebtsov, Evgeny; Zajnulina, Marina; Kandurova, Ksenia; Potapova, Elena; Dremin, Viktor; Mamoshin, Andrian; Sokolovski, Sergei; Dunaev, Andrey; Rafailov, Edik U. 2020. "Machine Learning Aided Photonic Diagnostic System for Minimally Invasive Optically Guided Surgery in the Hepatoduodenal Area" Diagnostics 10, no. 11: 873.

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