Mr. Sergey Agdarov Profile

Sergey Agdarov

Researcher at Bar-Ilan Univ

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Sergey Agdarov received her MSc degree in mechanical and mathematical from the Tajiki State University of Dushanbe, The Tajikistan, in 1987. Since 2011, he work as a researcher in the Engineering Faculty of Bar-Ilan University, Ramat-Gan, The Israel. Research involves the development, testing and evaluation of a biomedical optics, non-invasive measurement IOP, breast cancer early detection, silicon based mechanic-photonic wavelength converter, fiber sensor for non-contact estimation of vital bio-signs.

SPIE Journal Paper | 29 March 2024 Open Access

Remote and low-cost intraocular pressure monitoring by deep learning of speckle patterns

Zeev Kalyuzhner, Sergey Agdarov, Yevgeny Beiderman, Aviya Bennet, Yafim Beiderman, Zeev Zalevsky

JBO, Vol. 29, Issue 03, 037003, (March 2024) https://doi.org/10.1117/12.10.1117/1.JBO.29.3.037003

KEYWORDS: Mercury, Eye, Data modeling, Speckle pattern, Glaucoma, Education and training, Eye models, Deep learning, Sclera, Video

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SPIE Journal Paper | 1 August 2023 Open Access

Noninvasive blood glucose sensing by secondary speckle pattern artificial intelligence analyses

Deep Pal, Amitesh Kumar, Nave Avraham, Yoram Eisenbach, Yevgeny Beiderman, Sergey Agdarov, Yafim Beiderman, Zeev Zalevsky

JBO, Vol. 28, Issue 08, 087001, (August 2023) https://doi.org/10.1117/12.10.1117/1.JBO.28.8.087001

KEYWORDS: Glucose, Blood, Magnetism, Speckle pattern, Data modeling, Education and training, Skin, Speckle, Plasma, Reflection

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SignificanceDiabetes is a prevalent disease worldwide that can cause severe health problems. Accurate blood glucose detection is crucial for diabetes management, and noninvasive methods can be more convenient and less painful than traditional finger-prick methods.AimWe aim to report a noncontact speckle-based blood glucose measurement system that utilizes artificial intelligence (AI) data processing to improve glucose detection accuracy. The study also explores the influence of an alternating current (AC) induced magnetic field on the sensitivity and selectivity of blood glucose detection.ApproachThe proposed blood glucose sensor consists of a digital camera, an AC-generated magnetic field source, a laser illuminating the subject’s finger, and a computer. A magnetic field is applied to the finger, and a camera records the speckle patterns generated by the laser light reflected from the finger. The acquired video data are preprocessed for machine learning (ML) and deep neural networks (DNNs) to classify blood plasma glucose levels. The standard finger-prick method is used as a reference for blood glucose level classification.ResultsThe study found that the noncontact speckle-based blood glucose measurement system with AI data processing allows for the detection of blood plasma glucose levels with high accuracy. The ML approach gives better results than the tested DNNs as the proposed data preprocessing is highly selective and efficient.ConclusionsThe proposed noncontact blood glucose sensing mechanism utilizing AI data processing and a magnetic field can potentially improve glucose detection accuracy, making it more convenient and less painful for patients. The system also allows for inexpensive blood glucose sensing mechanisms and fast blood glucose screening. The results suggest that noninvasive methods can improve blood glucose detection accuracy, which can have significant implications for diabetes management. Investigations involving representative sampling data, including subjects of different ages, gender, race, and health status, could allow for further improvement.

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Proceedings Article | 4 March 2022 Presentation

Wearable vs. contact-free photonic bio-sensors and their application to SARS-CoV-2 symptoms detection

Zeev Kalyuzhner, Yafim Beiderman, Sergey Agdarov, Zeev Zalevsky

Proceedings Volume PC11979, PC1197904 (2022) https://doi.org/10.1117/12.2614647

KEYWORDS: Sensors, Biosensors, Biomedical optics, Artificial intelligence, Evolutionary algorithms, Speckle pattern, Sclera, Oxygen, Optical sensing, Lung

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In this presentation we will present two types of sensors for detecting SARS-CoV-2 symptoms. The first part of the presentation will address a contact-free sensor while its operation principle involves illuminating the inspected subject with a laser beam and analyzing with artificial intelligence (AI) based algorithms, the temporal-spatial changes occurring in the back scattered secondary 2D speckle patterns captured through properly defocused optics. The sensing is performed from a distance of several meters away and is applied to different regions of the subject’s body. We demonstrate measurements performed from the chest and then we extract various cardio-pulmonary bio-sign (several simultaneously) including the sounds of subject’s heart and lungs (like a remote stethoscope). We also perform measurements from the sclera and search for anomalies in the random eye movements. From those anomalies we estimate amount of saturated oxygen in the blood stream. All of the above-mentioned bio-parameters could be useful for remote early detection of SARS-CoV-2 symptoms. The AI algorithms are applied not only to extract the various bio-signs but also to perform the bio-medical diagnosis. In the second part of the presentation, we will present fiber based sensor that is incorporated into textile and clothing and make them a smart-clothing capable via a non-tight contact way to perform sensing of various vital bio-signs (several simultaneously). The bio-parameters to be sensed are related to cardio-pulmonary activity as well as blood-pressure and thus could be associated with early detection of SARS-CoV-2 symptoms. The fiber sensor is based on enhanced multi-mode fiber while at its output an artificial intelligence (AI) based algorithm analyses the temporal-spatial characterizations of the generated dynamic 2D speckle patterns. The fiber sensors are positioned in several locations in the clothing and can perform the bio-measurement from different organs of the wearer and thus allow a comparative measurement which could assist in obtaining more agnostic and more reliable bio-sensing. The AI algorithms are applied not only to extract the various bio-signs but also to perform the bio-medical diagnosis.

Proceedings Article | 21 February 2020 Presentation + Paper

Fiber-integrated fabric for non-tight contact bio-sensing of vital signs

Aviya Bennet, Rotem Hendel, Barak Straussman, Yafim Beiderman, Sergey Agdarov, Zeev Zalevsky

Proceedings Volume 11258, 112580D (2020) https://doi.org/10.1117/12.2547059

KEYWORDS: Optical fibers, Speckle pattern, Sensors, Blood pressure, Algorithm development, Cameras, Detection and tracking algorithms, Modulation, Signal to noise ratio

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SPIE Journal Paper | 3 December 2019 Open Access

Corneal thickness measurement by secondary speckle tracking and image processing using machine-learning algorithms

Aviya Bennett, Elnatan Davidovitch, Yafim Beiderman, Sergey Agdarov, Yevgeny Beiderman, Avital Moshkovitz, Uri Polat, Zeev Zalevsky

JBO, Vol. 24, Issue 12, 126001, (December 2019) https://doi.org/10.1117/12.10.1117/1.JBO.24.12.126001

KEYWORDS: Commercial off the shelf technology, Speckle, Eye, Image processing, Speckle pattern, Cameras, Cornea, Light scattering, Scattering, Network architectures

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SPIE Journal Paper | 15 November 2018 Open Access

Intraocular pressure remote photonic biomonitoring based on temporally encoded external sound wave stimulation

Aviya Bennett, Yevgeny Beiderman, Sergey Agdarov, Yafim Beiderman, Nisan Ozana, Michael Belkin, Zeev Zalevsky

JBO, Vol. 23, Issue 11, 117001, (November 2018) https://doi.org/10.1117/12.10.1117/1.JBO.23.11.117001

KEYWORDS: Eye, Biomedical optics, Cornea, Speckle pattern, Cameras, Eye models, Calibration, Speckle, Acoustics, Signal to noise ratio

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Proceedings Article | 20 February 2018 Open Access

Breast cancer early detection via tracking of skin back-scattered secondary speckle patterns

Aviya Bennett, Talia Sirkis, Yevgeny Beiderman, Sergey Agdarov, Yafim Beiderman, Zeev Zalevsky

Proceedings Volume 10506, 1050603 (2018) https://doi.org/10.1117/12.2286791

KEYWORDS: Skin, Breast cancer, Tissues, Breast, Speckle pattern, Tumors, Tissue optics, Acoustics, Cancer, Natural surfaces

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Proceedings Article | 22 February 2017 Presentation + Paper

Blood pulse wave velocity and pressure sensing via fiber based and free space based optical sensors

Talia Sirkis, Yevgeny Beiderman, Sergey Agdarov, Yafim Beiderman, Zeev Zalevsky

Proceedings Volume 10077, 100770A (2017) https://doi.org/10.1117/12.2251602

KEYWORDS: Sensors, Optical sensors, Fiber optics sensors, Speckle pattern, Blood pressure, Heart, Tissue optics, Calibration

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