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

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.

Optical Diagnostics in Human Diseases

“…Zherebtsov and colleagues, according to the multimodal approach, described the development of the technical implementation and assessment of machine learning methods’ efficiency for the real-time diagnosis of tumors in hepatoduodenal organs by FS and LDF. This approach is aimed at improving the effectiveness of minimally invasive surgical operations by providing additional diagnostic information online [ 1 ]. Two articles are devoted to the topical problem of breast cancer’s early detection, including during surgery.…”

Intraoperative Optical Diagnostics of Uterine Microcirculation during Myomectomy

“…The issues of surgical tactics, including the need to preserve the pseudocapsule when removing the myoma during the laparoscopic intracapsular myomectomy [8], and the development of substantiated medical grounds for myomectomy in women with intramural myomas who are planning pregnancy, remain important [9]. The application of optical technologies, which are currently being successfully introduced in minimally invasive surgery [10][11][12][13], may help to solve some fundamental and applied problems in gynecology.…”

Polarization-Based Histopathology Classification of Ex Vivo Colon Samples Supported by Machine Learning

“…The latter could be utilized to mimic human-like intellect when handling large and complex datasets, images, etc. Being part of AI, the vastly expanding field of machine learning (ML) covers a wide spectrum of applications for solving multiple scientific problems [47][48][49][50][51][52][53] as well as for cancer classification [54][55][56][57][58][59][60][61][62]. Since conventional programming processes an input data by means of particular syntax and semantics to produce a desired output, such a method is prone to multiple errors repetition.…”