Open Access

Diagnostics, volume 10, issue 11, pages 873

Machine learning aided photonic diagnostic system for minimally invasive optically guided surgery in the hepatoduodenal area

Zherebtsov Evgeny A. ^{1, 2}

Zajnulina Marina ³

Kandurova Ksenia ¹

Potapova Elena V. ¹

Dremin Viktor ^{1, 3}

Mamoshin Andrian ^{1, 4}

Sokolovski Sergei ³

Dunaev Andrey V. ¹

Rafailov Edik ³

Hide authors affiliations Show authors affiliations: 4 affiliations

Research and Development Center of Biomedical Photonics, Orel State University, 302026 Orel, Russia |

Faculty of Information Technology and Electrical Engineering, University of Oulu, Optoelectronics and Measurement Techniques Unit, 90570 Oulu, Finland |

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 |

Publication type: Journal Article

Publication date: 2020-10-27

Multidisciplinary Digital Publishing Institute (MDPI)

Journal: Diagnostics

Quartile SCImago

Quartile WOS

Impact factor: 3.6

ISSN: 20754418

DOI: 10.3390/diagnostics10110873

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PubMed ID: 33121013

Clinical Biochemistry

Abstract

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.

By date By citations

Citations by journals

	1 2
Diagnostics	Diagnostics, 2, 28.57% Diagnostics 2 publications, 28.57%
Biomedical Optics Express	Biomedical Optics Express, 1, 14.29% Biomedical Optics Express 1 publication, 14.29%
Progress in Biomedical Optics and Imaging - Proceedings of SPIE	Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 1, 14.29% Progress in Biomedical Optics and Imaging - Proceedings of SPIE 1 publication, 14.29%
Frontiers in Physics	Frontiers in Physics, 1, 14.29% Frontiers in Physics 1 publication, 14.29%
Lasers in Surgery and Medicine	Lasers in Surgery and Medicine, 1, 14.29% Lasers in Surgery and Medicine 1 publication, 14.29%
	1 2

Citations by publishers

	1 2
Multidisciplinary Digital Publishing Institute (MDPI)	Multidisciplinary Digital Publishing Institute (MDPI), 2, 28.57% Multidisciplinary Digital Publishing Institute (MDPI) 2 publications, 28.57%
Optical Society of America	Optical Society of America, 1, 14.29% Optical Society of America 1 publication, 14.29%
SPIE	SPIE, 1, 14.29% SPIE 1 publication, 14.29%
Frontiers Media S.A.	Frontiers Media S.A., 1, 14.29% Frontiers Media S.A. 1 publication, 14.29%
Wiley	Wiley, 1, 14.29% Wiley 1 publication, 14.29%
	1 2

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Zherebtsov E. A. et al. Machine learning aided photonic diagnostic system for minimally invasive optically guided surgery in the hepatoduodenal area // Diagnostics. 2020. Vol. 10. No. 11. p. 873.

GOST all authors (up to 50) Copy

Zherebtsov E. A., Zajnulina M., Kandurova K., Potapova E. V., Dremin V., Mamoshin A., Sokolovski S., Dunaev A. V., Rafailov E. Machine learning aided photonic diagnostic system for minimally invasive optically guided surgery in the hepatoduodenal area // Diagnostics. 2020. Vol. 10. No. 11. p. 873.

RIS |

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RIS Copy

TY - JOUR

DO - 10.3390/diagnostics10110873

UR - https://doi.org/10.3390%2Fdiagnostics10110873

TI - Machine learning aided photonic diagnostic system for minimally invasive optically guided surgery in the hepatoduodenal area

T2 - Diagnostics

AU - Mamoshin, Andrian

AU - Sokolovski, Sergei

AU - Zajnulina, Marina

AU - Zherebtsov, Evgeny A.

AU - Kandurova, Ksenia

AU - Potapova, Elena V.

AU - Dremin, Viktor

AU - Dunaev, Andrey V.

AU - Rafailov, Edik

PY - 2020

DA - 2020/10/27 00:00:00

PB - Multidisciplinary Digital Publishing Institute (MDPI)

SP - 873

IS - 11

VL - 10

PMID - 33121013

SN - 2075-4418

ER -

BibTex |

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@article{2020_Zherebtsov,

author = {Andrian Mamoshin and Sergei Sokolovski and Marina Zajnulina and Evgeny A. Zherebtsov and Ksenia Kandurova and Elena V. Potapova and Viktor Dremin and Andrey V. Dunaev and Edik Rafailov},

title = {Machine learning aided photonic diagnostic system for minimally invasive optically guided surgery in the hepatoduodenal area},

journal = {Diagnostics},

year = {2020},

volume = {10},

publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},

month = {oct},

url = {https://doi.org/10.3390%2Fdiagnostics10110873},

number = {11},

pages = {873},

doi = {10.3390/diagnostics10110873}

}

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Zherebtsov, Evgeny A., et al. “Machine learning aided photonic diagnostic system for minimally invasive optically guided surgery in the hepatoduodenal area.” Diagnostics, vol. 10, no. 11, Oct. 2020, p. 873. https://doi.org/10.3390%2Fdiagnostics10110873.

Found error?

Publisher

Multidisciplinary Digital Publishing Institute (MDPI)

Journal

Diagnostics

Quartile SCImago

Quartile WOS

Impact factor

3.6

ISSN

20754418 (Electronic)

Labs

Scientific and Technological Center of Biomedical Photonics

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