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Sensing, Monitoring and Imaging Technologies for Diabetes and/or Peripheral Vascular Disease

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 13026

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Special Issue Editors

Prof. Dr. Frank L. Bowling

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Guest Editor

Division of Diabetes, Endocrinology and Gastroenterology, Division of Cardiovascular Sciences, School of Medical Sciences, University of Manchester, Manchester Academic Health Science Centre and Manchester Vascular Centre, Manchester University NHS Foundation Trust, Manchester, UK
Interests: vascular surgery; diabetes mellitus; diabetic foot; diabetic neuropathies; gait; joint mobility; median neuropathy

Dr. Steven Rogers

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Guest Editor

Division of Cardiovascular Sciences, School of Medical Sciences, University of Manchester, Manchester Academic Health Science Centre and Manchester Vascular Centre, Manchester University NHS Foundation Trust, Manchester, UK
Interests: vascular ultrasound; 3D Ultrasound; contrast-enhanced ultrasound; vascular imaging

Special Issue Information

Dear Colleagues,

Globally, 463 million people are affected by diabetes, a number estimated to increase by 700 million by 2045. People with diabetes are 2-3 times more likely to develop cardiovascular disease (CVD), which is the leading cause of death throughout the Western world, accounting for 54% of all deaths in developed countries and 45% in Europe. Peripheral vascular disease (PVD), namely, peripheral artery disease (PAD) and carotid artery disease, are significant contributors to CVD-related morbidity/mortality linked to atherosclerosis.

The increasing prevalence of diabetes and PVD must be considered a silent pandemic with significant, long-term and economic costs. Early sensing, screening and detection are, therefore, paramount to preventing cardiovascular events and, thus, CVD-related deaths. Additionally, accurate and novel imaging able to rapidly be deployed and upscaled is crucial to timely treatment planning and intervention.

It is well recognized that current PVD detection methods, such as ankle-brachial pressure testing, are limited in people with diabetes. However, continuous glucose monitoring/sensing with/out an artificial pancreas (closed-loop system) have been immensely successful.

This Special Issue focuses on any novel technique or approach (including the interesting utilization of existing technologies) for the early detection, screening, sensing or imaging of either diabetes and/or PVD.

Prof. Dr. Frank L. Bowling
Dr. Steven Rogers
Guest Editors

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Keywords

diabetes
peripheral vascular disease
carotid artery disease
peripheral arterial disease
sensing
early detection
screening
imaging
treatment planning

Published Papers (5 papers)

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Research

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14 pages, 2793 KiB

Open AccessArticle

An Intelligent Diabetic Patient Tracking System Based on Machine Learning for E-Health Applications

by Sindhu P. Menon, Prashant Kumar Shukla, Priyanka Sethi, Areej Alasiry, Mehrez Marzougui, M. Turki-Hadj Alouane and Arfat Ahmad Khan

Sensors 2023, 23(6), 3004; https://doi.org/10.3390/s23063004 - 10 Mar 2023

Cited by 8 | Viewed by 3357

Abstract

Background: Continuous surveillance helps people with diabetes live better lives. A wide range of technologies, including the Internet of Things (IoT), modern communications, and artificial intelligence (AI), can assist in lowering the expense of health services. Due to numerous communication systems, it is now possible to provide customized and distant healthcare. Main problem: Healthcare data grows daily, making storage and processing challenging. We provide intelligent healthcare structures for smart e-health apps to solve the aforesaid problem. The 5G network must offer advanced healthcare services to meet important requirements like large bandwidth and excellent energy efficacy. Methodology: This research suggested an intelligent system for diabetic patient tracking based on machine learning (ML). The architectural components comprised smartphones, sensors, and smart devices, to gather body dimensions. Then, the preprocessed data is normalized using the normalization procedure. To extract features, we use linear discriminant analysis (LDA). To establish a diagnosis, the intelligent system conducted data classification utilizing the suggested advanced-spatial-vector-based Random Forest (ASV-RF) in conjunction with particle swarm optimization (PSO). Results: Compared to other techniques, the simulation’s outcomes demonstrate that the suggested approach offers greater accuracy. Full article

(This article belongs to the Special Issue Sensing, Monitoring and Imaging Technologies for Diabetes and/or Peripheral Vascular Disease)

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11 pages, 1259 KiB

Open AccessArticle

A Pilot Study of Heart Rate Variability Synchrony as a Marker of Intraoperative Surgical Teamwork and Its Correlation to the Length of Procedure

by Katarzyna Powezka, Allan Pettipher, Apit Hemakom, Tricia Adjei, Pasha Normahani, Danilo P. Mandic and Usman Jaffer

Sensors 2022, 22(22), 8998; https://doi.org/10.3390/s22228998 - 21 Nov 2022

Cited by 4 | Viewed by 1487

Abstract

Objective: Quality of intraoperative teamwork may have a direct impact on patient outcomes. Heart rate variability (HRV) synchrony may be useful for objective assessment of team cohesion and good teamwork. The primary aim of this study was to investigate the feasibility of using HRV synchrony in surgical teams. Secondary aims were to investigate the association of HRV synchrony with length of procedure (LOP), complications, number of intraoperative glitches and length of stay (LOS). We also investigated the correlation between HRV synchrony and team familiarity, pre- and intraoperative stress levels (STAI questionnaire), NOTECHS score and experience of team members. Methods: Ear, nose and throat (ENT) and vascular surgeons (consultant and registrar team members) were recruited into the study. Baseline demographics including level of team members’ experience were gathered before each procedure. For each procedure, continuous electrocardiogram (ECG) recording was performed and questionnaires regarding pre- and intraoperative stress levels and non-technical skills (NOTECHS) scores were collected for each team member. An independent observer documented the time of each intraoperative glitch. Statistical analysis was conducted using stepwise multiple linear regression. Results: Four HRV synchrony metrics which may be markers of efficient surgical collaboration were identified from the data: 1. number of HRV synchronies per hour of procedure, 2. number of HRV synchrony trends per hour of procedure, 3. length of HRV synchrony trends per hour of procedure, 4. area under the HRV synchrony trend curve per hour of procedure. LOP was inversely correlated with number of HRV synchrony trends per hour of procedure (p < 0.0001), area under HRV synchrony trend curve per hour of procedure (p = 0.001), length of HRV synchrony trends per hour of procedure (p = 0.002) and number of HRV synchronies per hour of procedure (p < 0.0001). LOP was positively correlated with: FS (p = 0.043; R = 0.358) and intraoperative STAI score of the whole team (p = 0.007; R = 0.493). Conclusions: HRV synchrony metrics within operating teams may be used as an objective marker to quantify surgical teamwork. We have shown that LOP is shorter when the intraoperative surgical teams’ HRV is more synchronised. Full article

(This article belongs to the Special Issue Sensing, Monitoring and Imaging Technologies for Diabetes and/or Peripheral Vascular Disease)

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11 pages, 1337 KiB

Open AccessArticle

SUDOSCAN, an Innovative, Simple and Non-Invasive Medical Device for Assessing Sudomotor Function

by Dana Elena Gavan, Alexandru Gavan, Cosmina Ioana Bondor, Bogdan Florea, Frank Lee Bowling, Georgeta Victoria Inceu and Liora Colobatiu

Sensors 2022, 22(19), 7571; https://doi.org/10.3390/s22197571 - 06 Oct 2022

Cited by 7 | Viewed by 2269

Abstract

Diabetic autonomic neuropathy is probably the most undiagnosed but serious complication of diabetes. The main objectives were to assess the prevalence of peripheral and autonomic neuropathy in a population of diabetic patients, analyze it in a real-life outpatient unit scenario and determine the feasibility of performing SUDOSCAN tests together with widely used tests for neuropathy. A total of 33 patients were included in the study. Different scoring systems (the Toronto Clinical Neuropathy Score—TCNS; the Neuropathy Disability Score—NDS; and the Neuropathy Symptom Score—NSS) were applied to record diabetic neuropathy (DN), while the SUDOSCAN medical device was used to assess sudomotor function, detect diabetic autonomic neuropathy and screen for cardiac autonomic neuropathy (CAN). Fifteen (45.5%) patients had sudomotor dysfunction. The SUDOSCAN CAN risk score was positively correlated with the hands’ electrochemical sweat conductance (ESC), diastolic blood pressure (DBP), the level of the glycated hemoglobin, as well as with the TCNS, NDS and NSS. Performing SUDOSCAN tests together with other tests for DN proved to be a feasible approach that could be used in daily clinical practice in order to screen for DN, as well as for the early screening of CAN, before more complex and time-consuming tests. Full article

(This article belongs to the Special Issue Sensing, Monitoring and Imaging Technologies for Diabetes and/or Peripheral Vascular Disease)

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29 pages, 12567 KiB

Open AccessArticle

A Signal Processing Method for Assessing Ankle Torque with a Custom-Made Electronic Dynamometer in Participants Affected by Diabetic Peripheral Neuropathy

by Iulia Iovanca Dragoi, Teodor Petrita, Florina Georgeta Popescu, Florin Alexa, Sorin Barac, Frank L. Bowling, Neil D. Reeves, Cosmina Ioana Bondor and Mihai Ionac

Sensors 2022, 22(16), 6310; https://doi.org/10.3390/s22166310 - 22 Aug 2022

Viewed by 1921

Abstract

Portable, custom-made electronic dynamometry for the foot and ankle is a promising assessment method that enables foot and ankle muscle function to be established in healthy participants and those affected by chronic conditions. Diabetic peripheral neuropathy (DPN) can alter foot and ankle muscle function. This study assessed ankle toque in participants with diabetic peripheral neuropathy and healthy participants, with the aim of developing an algorithm for optimizing the precision of data processing and interpretation of the results and to define a reference frame for ankle torque measurement in both healthy participants and those affected by DPN. This paper discloses the software chain and the signal processing methods used for voltage—torque conversion, filtering, offset detection and the muscle effort type identification, which further allowed for a primary statistical report. The full description of the signal processing methods will make our research reproducible. The applied algorithm for signal processing is proposed as a reference frame for ankle torque assessment when using a custom-made electronic dynamometer. While evaluating multiple measurements, our algorithm permits for a more detailed parametrization of the ankle torque results in healthy participants and those affected by DPN. Full article

(This article belongs to the Special Issue Sensing, Monitoring and Imaging Technologies for Diabetes and/or Peripheral Vascular Disease)

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Review

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34 pages, 703 KiB

Open AccessReview

Noninvasive Glucose Sensing In Vivo

by Ho Man Colman Leung, Gregory P. Forlenza, Temiloluwa O. Prioleau and Xia Zhou

Sensors 2023, 23(16), 7057; https://doi.org/10.3390/s23167057 - 09 Aug 2023

Cited by 4 | Viewed by 3150

Abstract

Blood glucose monitoring is an essential aspect of disease management for individuals with diabetes. Unfortunately, traditional methods require collecting a blood sample and thus are invasive and inconvenient. Recent developments in minimally invasive continuous glucose monitors have provided a more convenient alternative for people with diabetes to track their glucose levels 24/7. Despite this progress, many challenges remain to establish a noninvasive monitoring technique that works accurately and reliably in the wild. This review encompasses the current advancements in noninvasive glucose sensing technology in vivo, delves into the common challenges faced by these systems, and offers an insightful outlook on existing and future solutions. Full article

(This article belongs to the Special Issue Sensing, Monitoring and Imaging Technologies for Diabetes and/or Peripheral Vascular Disease)

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