University of Oulu

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

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

Saved in:
Author: Zherebtsov, Evgeny1,2; Zajnulina, Marina3; Kandurova, Ksenia1;
Organizations: 1Research and Development Center of Biomedical Photonics, Orel State University, 302026 Orel, Russia
2Faculty of Information Technology and Electrical Engineering, University of Oulu, Optoelectronics and Measurement Techniques Unit, 90570 Oulu, Finland
3Aston Institute of Photonic Technologies, Aston University, Birmingham B4 7ET, UK
4Department of X-ray Surgical Methods of Diagnosis and Treatment, Orel Regional Clinical Hospital, 302028 Orel, Russia
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 3.4 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe20201215100722
Language: English
Published: Multidisciplinary Digital Publishing Institute, 2020
Publish Date: 2020-12-15
Description:

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 %lt; 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.

see all

Series: Diagnostics
ISSN: 2075-4418
ISSN-E: 2075-4418
ISSN-L: 2075-4418
Volume: 10
Issue: 11
Article number: 873
DOI: 10.3390/diagnostics10110873
OADOI: https://oadoi.org/10.3390/diagnostics10110873
Type of Publication: A1 Journal article – refereed
Field of Science: 217 Medical engineering
3126 Surgery, anesthesiology, intensive care, radiology
Subjects:
Funding: This study was supported by the Russian Science Foundation under project No. 18-15-00201 (development of experimental setup and data acquisition). Special thanks are extended to the patients of the Orel Regional Clinical Hospital who kindly agreed to take part in the studies in the framework of their planned minimally invasive surgical intervention. M.Z. would like to acknowledge the funding received within the H2020-MSCA-IF-2017 scheme (grant No. 792421). E.Z. acknowledges the support of the Academy of Finland (grant No. 318281). V.D. acknowledges the funding received within the H2020-MSCA-IF-2018 scheme (grant No. 839888).
Academy of Finland Grant Number: 318281
Detailed Information: 318281 (Academy of Finland Funding decision)
Copyright 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 (http://creativecommons.org/licenses/by/4.0/).
  https://creativecommons.org/licenses/by/4.0/