Remote photonic sensing of glucose concentration via analysis of time varied speckle patterns

Ozana, Nisan; Talman, Roy; Shemer, Amir; Schwartz, Adi; Polani, Sagi; Califa, Ran; Beiderman, Yevgeny; Ruiz-Rivas, J.; Garc, Javier

doi:10.5185/amlett.2018.2054

Cited by 4 publications

(10 citation statements)

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“…The third type is the object's tilting, which is expressed as shifts in the pattern of speckles, as we can see in the light distribution. 13,19,20 Therefore, the overall effect of the three types of movement is only a shift in the transversal plane. The speckle patterns' temporal trajectories are proportional to the signals to be extracted, the frequent change in the blood vessels, and the interaction between the coherent light and the illuminated surface, such as human skin affecting recorded speckle patterns.…”

Section: Speckle Pattern Analysesmentioning

confidence: 99%

“…The speckle patterns' temporal trajectories are proportional to the signals to be extracted, the frequent change in the blood vessels, and the interaction between the coherent light and the illuminated surface, such as human skin affecting recorded speckle patterns. 13,15,16,20 2.2 Speckle-Based Blood Glucose Sensing Blood has four main components: plasma, red blood cells (RBCs), white blood cells, and platelets. Blood has many different functions, including transporting oxygen and nutrients to the lungs and tissues and forming blood clots to prevent excess blood loss.…”

Section: Speckle Pattern Analysesmentioning

confidence: 99%

“…In addition, the glucose concentration affects the blood pulse stream, including the tilt of the skin affecting the speckle patterns. 2,3,13,23 A light beam's polarization rotation angle φ changes as it passes through magneto-optic materials, and it is given as 14,22 E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 0 1 ; 1 1 7 ; 4 6 0…”

Section: Speckle Pattern Analysesmentioning

confidence: 99%

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Noninvasive blood glucose sensing by secondary speckle pattern artificial intelligence analyses

Pal,

Kumar,

Avraham

et al. 2023

J. Biomed. Opt.

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Diabetes 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. Aim:We 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. Approach:The 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. Results:The 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. Conclusions:The 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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Section: Speckle Pattern Analysesmentioning

confidence: 99%

Section: Speckle Pattern Analysesmentioning

confidence: 99%

Section: Speckle Pattern Analysesmentioning

confidence: 99%

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Noninvasive blood glucose sensing by secondary speckle pattern artificial intelligence analyses

Pal,

Kumar,

Avraham

et al. 2023

J. Biomed. Opt.

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“…Defocused speckle imaging is an optical method in which a defocused camera records the scattered interference of the primary speckle pattern that characterizes the modulation in the sensing fiber or secondary-reflected from a surface illuminated by a laser beam. 8,10,20 Speckle motion tracking enables the detection of disturbances in the sensing fiber from a distance. 17 The multimode fibers interacting with the tissue under different glucose levels change the speckle patterns.…”

Section: Analysis Of Recorded Speckle Patternsmentioning

confidence: 99%

“…The demand for a cost-effective, compact, painless, convenient, non-invasive device to relieve pain by monitoring glucose levels without pricking the skin barrier has increased. 9 , 10 In recent years, non-invasive devices for measuring human blood glucose levels have been developed; these include the MediWise GlucoWise, Nemaura Medical SugerBEAT, Cnoga Medical (Combo Glucometer), Integrity Applications, C8 MediSensors, and OrSense (NBM-200G). The devices use different optical, thermal, transdermal (electrochemical), and microwave methods for non-invasive glucose monitoring.…”

Section: Introductionmentioning

confidence: 99%

Non-invasive blood glucose sensing by machine learning of optic fiber-based speckle pattern variation

et al. 2022

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. Significance: The ability to perform frequent non-invasive monitoring of glucose in the bloodstream is very applicable for diabetic patients. Aim: We experimentally verified a non-invasive multimode fiber-based technique for sensing glucose concentration in the bloodstream by extracting and analyzing the collected speckle patterns. Approach: The proposed sensor consists of a laser source, digital camera, computer, multimode fiber, and alternating current (AC) generated magnetic field source. The experiments were performed using a covered (with cladding and jacket) and uncovered (without cladding and jacket) multimode fiber touching the skin under a magnetic field and without it. The subject’s finger was placed on a fiber to detect the glucose concentration. The method tracks variations in the speckle patterns due to light interaction with the bloodstream affected by blood glucose. Results: The uncovered fiber placed above the finger under the AC magnetic field (150 G) at 140 Hz was found to have a lock-in amplification role, improving the glucose detection precision. The application of the machine learning algorithms in preprocessed speckle pattern data increase glucose measurement accuracy. Classification of the speckle patterns for uncovered fiber under the AC magnetic field allowed for detection of the blood glucose with high accuracy for all tested subjects compared with other tested configurations. Conclusions: The proposed technique was theoretically analyzed and experimentally validated in this work. The results were verified by the traditional finger-prick method, which was also used for classification as a conventional reference marker of blood glucose levels. The main goal of the proposed technique was to develop a non-invasive, low-cost blood glucose sensor for easy use by humans.

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Non-invasive screening of glycemic state by statistical analysis of speckle images

Gubnitsky

Rozenberg

Rosenzweig

et al. 2023

Optics Communications

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Remote photonic sensing of glucose concentration via analysis of time varied speckle patterns

Cited by 4 publications

References 0 publications

Noninvasive blood glucose sensing by secondary speckle pattern artificial intelligence analyses

Noninvasive blood glucose sensing by secondary speckle pattern artificial intelligence analyses

Non-invasive blood glucose sensing by machine learning of optic fiber-based speckle pattern variation

Non-invasive screening of glycemic state by statistical analysis of speckle images

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