Ultrasound scattering by aggregated red blood cells in patients with diabetes

Sakaki, Hiroki; Arakawa, Mototaka; Yashiro, Satoshi; Todate, Yusuke; Ishigaki, Yasushi; Kanai, Hiroshi

doi:10.1007/s10396-018-0892-z

Cited by 10 publications

(5 citation statements)

References 24 publications

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“…NIO-GM devices have been proposed based on primary or direct glucose sensing as well as secondary, or indirect glucose sensing. Primary measurements involve collecting a signal derived directly from the glucose molecule, while secondary measurements involve measuring a parameter impacted by the concentration of glucose, such as (1) heart rate changes with electrocardiography, 21 (2) rate of red blood cell aggregation with ultrasound, 22 (3) blood volume dynamics with photoplethysmographic measurement of blood, 23,24 (4) dielectric properties of the skin matrix with diffuse scattering or temperature-modulated localized reflectance, 25 or (5) sudomotor dysfunction with electrochemical skin conductance and sweating asymmetry. 26 No NIO-GM product has received clearance by the United States Food and Drug Administration (FDA).…”

Section: Technological Approaches For Glucose Monitoring Productsmentioning

confidence: 99%

Products for Monitoring Glucose Levels in the Human Body With Noninvasive Optical, Noninvasive Fluid Sampling, or Minimally Invasive Technologies

Shang

Zhang

Thomas³

et al. 2021

J Diabetes Sci Technol

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Background: Conventional home blood glucose measurements require a sample of blood that is obtained by puncturing the skin at the fingertip. To avoid the pain associated with this procedure, there is high demand for medical products that allow glucose monitoring without blood sampling. In this review article, all such products are presented. Methods: In order to identify such products, four different sources were used: (1) PubMed, (2) Google Patents, (3) Diabetes Technology Meeting Startup Showcase participants, and (4) experts in the field of glucose monitoring. The information obtained were filtered by using two inclusion criteria: (1) regulatory clearance, and/or (2) significant coverage in Google News starting in the year 2016, unless the article indicated that the product had been discontinued. The identified bloodless monitoring products were classified into three categories: (1) noninvasive optical, (2) noninvasive fluid sampling, and (3) minimally invasive devices. Results: In total, 28 noninvasive optical, 6 noninvasive fluid sampling, and 31 minimally invasive glucose monitoring products were identified. Subsequently, these products were characterized according to their regulatory, technological, and consumer features. Products with regulatory clearance are described in greater detail according to their advantages and disadvantages, and with design images. Conclusions: Based on favorable technological features, consumer features, and other advantages, several bloodless products are commercially available and promise to enhance diabetes management. Paths for future products are discussed with an emphasis on understanding existing barriers related to both technical and non-technical issues.

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Section: Technological Approaches For Glucose Monitoring Productsmentioning

confidence: 99%

Products for Monitoring Glucose Levels in the Human Body With Noninvasive Optical, Noninvasive Fluid Sampling, or Minimally Invasive Technologies

Shang

Zhang

Thomas³

et al. 2021

J Diabetes Sci Technol

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“…We have studied a noninvasive and quantitative method for measuring blood properties. [28][29][30][31][32][33][34][35][36][37] Saito et al assumed that RBCs aggregate as a single-sphere scatterer and estimated the size from the scattering characteristics extracted by normalizing the power spectrum measured from the vessel lumen with that from the rear wall using a focused ultrasound transducer. [28][29][30][31][32] We also examined the relationship between RBC aggregation using ultrasound and blood glucose level.…”

Section: Introductionmentioning

confidence: 99%

“…Sakaki et al compared the blood glucose levels with the parameters determined from changes in the scattering power spectra of the echoes from the vascular lumen before and after avascularization. 33,34) Fukase et al studied the relationship between the blood glucose levels and the brightness values of the B-mode image. 35) However, there are still problems, such as the low correlation between these parameters and the blood glucose levels and the lack of reproducibility of the results because of the variation in the data among the measurements.…”

Section: Introductionmentioning

confidence: 99%

Estimation of aggregate size of red blood cell by introducing reference power spectrum measured for hemispherical ultrafine wire

Higashiyama

Mori

Arakawa

et al. 2022

Jpn. J. Appl. Phys.

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Noninvasive measurement of the degree of red blood cell (RBC) aggregation is useful for evaluating blood properties. In the present paper, we proposed a method to estimate the size of RBC aggregates without using the power spectrum of the posterior wall by introducing a reference scattering spectrum. The reference power spectra were calculated using the power spectrum measured for an ultrafine wire with a hemispherical tip. They were applied to the size estimation of microparticles simulating RBC aggregates. The estimated sizes were close to the true values, which shows that the calculated reference power spectra were suitable for accurate size estimation. The proposed method was also applied to in vivo measurements, and the estimated sizes between at rest and in RBCs aggregated by avascularization were successfully differentiated. This demonstrates that the proposed method will be useful for estimating the size of RBC aggregates.

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“…Similarly, Hanna, J. et al experimented with glucose monitoring using electromagnetic devices in a noninvasive manner [14]. Other researchers utilized changes in bioimpedance [15] and ultrasound signal [16,17] to determine a subject's blood glucose level. In general, electrical techniques have room for improvement due to poor selectivity, temperature sensitivity, and skin irritation.…”

Section: Introductionmentioning

confidence: 99%

Blood Glucose Level Regression for Smartphone PPG Signals Using Machine Learning

et al. 2021

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Diabetes is a chronic illness that affects millions of people worldwide and requires regular monitoring of a patient’s blood glucose level. Currently, blood glucose is monitored by a minimally invasive process where a small droplet of blood is extracted and passed to a glucometer—however, this process is uncomfortable for the patient. In this paper, a smartphone video-based noninvasive technique is proposed for the quantitative estimation of glucose levels in the blood. The videos are collected steadily from the tip of the subject’s finger using smartphone cameras and subsequently converted into a Photoplethysmography (PPG) signal. A Gaussian filter is applied on top of the Asymmetric Least Square (ALS) method to remove high-frequency noise, optical noise, and motion interference from the raw PPG signal. These preprocessed signals are then used for extracting signal features such as systolic and diastolic peaks, the time differences between consecutive peaks (DelT), first derivative, and second derivative peaks. Finally, the features are fed into Principal Component Regression (PCR), Partial Least Square Regression (PLS), Support Vector Regression (SVR) and Random Forest Regression (RFR) models for the prediction of glucose level. Out of the four statistical learning techniques used, the PLS model, when applied to an unbiased dataset, has the lowest standard error of prediction (SEP) at 17.02 mg/dL.

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Ultrasound scattering by aggregated red blood cells in patients with diabetes

Abstract: The proposed method has potential for clinical application after elucidation of the various factors affecting RBC aggregation.

Cited by 10 publications

References 24 publications

Products for Monitoring Glucose Levels in the Human Body With Noninvasive Optical, Noninvasive Fluid Sampling, or Minimally Invasive Technologies

Products for Monitoring Glucose Levels in the Human Body With Noninvasive Optical, Noninvasive Fluid Sampling, or Minimally Invasive Technologies

Estimation of aggregate size of red blood cell by introducing reference power spectrum measured for hemispherical ultrafine wire

Blood Glucose Level Regression for Smartphone PPG Signals Using Machine Learning

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