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Glucose Sensors and Artificial Intelligence
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Glucose Sensors and Artificial Intelligence

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

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 16476

Special Issue Editors

Dr. Martina Vettoretti

E-Mail Website
Guest Editor
Department of Information Engineering, University of Padova, Via G. Gradenigo 6B, 35131 Padova PD, Italy
Interests: signal processing and modeling techniques for the analysis of glucose sensor data; strategies for type 1 diabetes insulin therapy optimization; statistical learning; machine-learning techniques applied to clinical predictive model development
Special Issues, Collections and Topics in MDPI journals
Dr. Giacomo Cappon

E-Mail Website
Guest Editor
Department of Information Engineering, University of Padova, Padova, Italy
Interests: machine learning; Bayesian methods; signal processing; decision support systems; wearable sensors; digital health and therapeutics; telemedicine
Special Issues, Collections and Topics in MDPI journals
Dr. Pau Herrero

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
Interests: diabetes technology; control engineering; machine learning; biomedical informatics; infectious diseases technology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Lucia Sacchi

E-Mail Website
Guest Editor
Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
Interests: Clinical Decision Support Systems; Personalized Medicine; Clinical Data Mining; Big Data Analytics; Temporal Abstractions; Temporal Association Rule Mining
Prof. Dr. Wendell Coltro

E-Mail Website
Guest Editor
Institute of Chemistry, Federal University of Goias, Goiania, Brazil
Interests: Design and characterization of low-cost platforms for human health
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Glucose sensors play a key role in the management of diabetes as a tool for detecting potentially dangerous conditions (such as hyperglycemia and hypoglycemia) and to inform therapeutic decisions. Three types of sensors are currently being used in diabetes management: self-monitoring of blood glucose (SMBG) sensors, intermittently scanned glucose monitoring sensors (isCGM), and real-time continuous glucose monitoring (rtCGM) sensors. In particular, rtCGM sensors measure glucose concentration in the interstitial fluid almost continuously for several consecutive days or weeks, thus providing a very rich picture of the glycemic profile of people with diabetes. Other types of glucose sensors are also being investigated that noninvasively measure glucose concentration in a number of biological fluids (e.g. tears, sweat, and saliva).

Glucose sensor data can be integrated with other subject-generated data (e.g. insulin doses, meals, and physical activity) and used for many applications, including event prediction, decision-support systems, and therapy optimization. Nevertheless, glucose sensor data can be affected by some inaccuracies, such as noise, lack of calibration, decline of sensor sensitivity, and artifacts, which need to be addressed to ensure data reliability.

The underlying complexity of glucose dynamics which is usually difficult to handle with traditional model-based methods, together with the large amount of glucose data currently being collected and the rapid advances in computational power, increasingly calls for the application of artificial intelligence (AI) techniques for the development of advanced glucose sensor-based applications.

We are pleased to announce the special issue entitled “Glucose sensors and artificial intelligence”, whose aim is to collect original papers and review papers about the use of AI techniques to analyze glucose sensor data. Topics of interest include, but are not limited to, the use of AI techniques for:

  • event detection (e.g. sensor faults, unannounced meals and exercise, stress, recurrent illness)
  • real-time prediction of glucose concentration levels and hyper/hypoglycemic events
  • pattern recognition and clustering
  • decision-support systems
  • therapy optimization and personalization
  • prediction of long-term complications

Dr. Martina Vettoretti
Dr. Giacomo Cappon
Dr. Pau Herrero
Prof. Dr. Lucia Sacchi
Prof. Dr. Wendell Coltro
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (5 papers)

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Research

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18 pages, 1007 KiB  
Article
A Deep Learning Framework for Automatic Meal Detection and Estimation in Artificial Pancreas Systems
by John Daniels, Pau Herrero and Pantelis Georgiou
Sensors 2022, 22(2), 466; https://0-doi-org.brum.beds.ac.uk/10.3390/s22020466 - 08 Jan 2022
Cited by 19 | Viewed by 3174
Abstract
Current artificial pancreas (AP) systems are hybrid closed-loop systems that require manual meal announcements to manage postprandial glucose control effectively. This poses a cognitive burden and challenge to users with T1D since this relies on frequent user engagement to maintain tight glucose control. [...] Read more.
Current artificial pancreas (AP) systems are hybrid closed-loop systems that require manual meal announcements to manage postprandial glucose control effectively. This poses a cognitive burden and challenge to users with T1D since this relies on frequent user engagement to maintain tight glucose control. In order to move towards fully automated closed-loop glucose control, we propose an algorithm based on a deep learning framework that performs multitask quantile regression, for both meal detection and carbohydrate estimation. Our proposed method is evaluated in silico on 10 adult subjects from the UVa/Padova simulator with a Bio-inspired Artificial Pancreas (BiAP) control algorithm over a 2 month period. Three different configurations of the AP are evaluated -BiAP without meal announcement (BiAP-NMA), BiAP with meal announcement (BiAP-MA), and BiAP with meal detection (BiAP-MD). We present results showing an improvement of BiAP-MD over BiAP-NMA, demonstrating 144.5 ± 6.8 mg/dL mean blood glucose level (−4.4 mg/dL, p< 0.01) and 77.8 ± 6.3% mean time between 70 and 180 mg/dL (+3.9%, p< 0.001). This improvement in control is realised without a significant increase in mean in hypoglycaemia (+0.1%, p= 0.4). In terms of detection of meals and snacks, the proposed method on average achieves 93% precision and 76% recall with a detection delay time of 38 ± 15 min (92% precision, 92% recall, and 37 min detection time for meals only). Furthermore, BiAP-MD handles hypoglycaemia better than BiAP-MA based on CVGA assessment with fewer control errors (10% vs. 20%). This study suggests that multitask quantile regression can improve the capability of AP systems for postprandial glucose control without increasing hypoglycaemia. Full article
(This article belongs to the Special Issue Glucose Sensors and Artificial Intelligence)
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18 pages, 8732 KiB  
Article
Machine Learning Methods of Regression for Plasmonic Nanoantenna Glucose Sensing
by Emilio Corcione, Diana Pfezer, Mario Hentschel, Harald Giessen and Cristina Tarín
Sensors 2022, 22(1), 7; https://0-doi-org.brum.beds.ac.uk/10.3390/s22010007 - 21 Dec 2021
Cited by 6 | Viewed by 3087
Abstract
The measurement and quantification of glucose concentrations is a field of major interest, whether motivated by potential clinical applications or as a prime example of biosensing in basic research. In recent years, optical sensing methods have emerged as promising glucose measurement techniques in [...] Read more.
The measurement and quantification of glucose concentrations is a field of major interest, whether motivated by potential clinical applications or as a prime example of biosensing in basic research. In recent years, optical sensing methods have emerged as promising glucose measurement techniques in the literature, with surface-enhanced infrared absorption (SEIRA) spectroscopy combining the sensitivity of plasmonic systems and the specificity of standard infrared spectroscopy. The challenge addressed in this paper is to determine the best method to estimate the glucose concentration in aqueous solutions in the presence of fructose from the measured reflectance spectra. This is referred to as the inverse problem of sensing and usually solved via linear regression. Here, instead, several advanced machine learning regression algorithms are proposed and compared, while the sensor data are subject to a pre-processing routine aiming to isolate key patterns from which to extract the relevant information. The most accurate and reliable predictions were finally made by a Gaussian process regression model which improves by more than 60% on previous approaches. Our findings give insight into the applicability of machine learning methods of regression for sensor calibration and explore the limitations of SEIRA glucose sensing. Full article
(This article belongs to the Special Issue Glucose Sensors and Artificial Intelligence)
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13 pages, 10553 KiB  
Article
90% Accuracy for Photoplethysmography-Based Non-Invasive Blood Glucose Prediction by Deep Learning with Cohort Arrangement and Quarterly Measured HbA1c
by Justin Chu, Wen-Tse Yang, Wei-Ru Lu, Yao-Ting Chang, Tung-Han Hsieh and Fu-Liang Yang
Sensors 2021, 21(23), 7815; https://0-doi-org.brum.beds.ac.uk/10.3390/s21237815 - 24 Nov 2021
Cited by 5 | Viewed by 3142
Abstract
Previously published photoplethysmography-(PPG) based non-invasive blood glucose (NIBG) measurements have not yet been validated over 500 subjects. As illustrated in this work, we increased the number subjects recruited to 2538 and found that the prediction accuracy (the ratio in zone A of Clarke’s [...] Read more.
Previously published photoplethysmography-(PPG) based non-invasive blood glucose (NIBG) measurements have not yet been validated over 500 subjects. As illustrated in this work, we increased the number subjects recruited to 2538 and found that the prediction accuracy (the ratio in zone A of Clarke’s error grid) reduced to undesirable 60.6%. We suspect the low prediction accuracy induced by larger sample size might arise from the physiological diversity of subjects, and one possibility is that the diversity might originate from medication. Therefore, we split the subjects into two cohorts for deep learning: with and without medication (1682 and 856 recruited subjects, respectively). In comparison, the cohort training for subjects without any medication had approximately 30% higher prediction accuracy over the cohort training for those with medication. Furthermore, by adding quarterly (every 3 months) measured glycohemoglobin (HbA1c), we were able to significantly boost the prediction accuracy by approximately 10%. For subjects without medication, the best performing model with quarterly measured HbA1c achieved 94.3% prediction accuracy, RMSE of 12.4 mg/dL, MAE of 8.9 mg/dL, and MAPE of 0.08, which demonstrates a very promising solution for NIBG prediction via deep learning. Regarding subjects with medication, a personalized model could be a viable means of further investigation. Full article
(This article belongs to the Special Issue Glucose Sensors and Artificial Intelligence)
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27 pages, 2635 KiB  
Article
Ensemble Models of Cutting-Edge Deep Neural Networks for Blood Glucose Prediction in Patients with Diabetes
by Félix Tena, Oscar Garnica, Juan Lanchares and Jose Ignacio Hidalgo
Sensors 2021, 21(21), 7090; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217090 - 26 Oct 2021
Cited by 11 | Viewed by 2054
Abstract
This article proposes two ensemble neural network-based models for blood glucose prediction at three different prediction horizons—30, 60, and 120 min—and compares their performance with ten recently proposed neural networks. The twelve models’ performances are evaluated under the same OhioT1DM Dataset, preprocessing workflow, [...] Read more.
This article proposes two ensemble neural network-based models for blood glucose prediction at three different prediction horizons—30, 60, and 120 min—and compares their performance with ten recently proposed neural networks. The twelve models’ performances are evaluated under the same OhioT1DM Dataset, preprocessing workflow, and tools at the three prediction horizons using the most common metrics in blood glucose prediction, and we rank the best-performing ones using three methods devised for the statistical comparison of the performance of multiple algorithms: scmamp, model confidence set, and superior predictive ability. Our analysis provides a comparison of the state-of-the-art neural networks for blood glucose prediction, estimating the model’s error, highlighting those with the highest probability of being the best predictors, and providing a guide for their use in clinical practice. Full article
(This article belongs to the Special Issue Glucose Sensors and Artificial Intelligence)
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Review

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18 pages, 2097 KiB  
Review
Colorimetric and Electrochemical Screening for Early Detection of Diabetes Mellitus and Diabetic Retinopathy—Application of Sensor Arrays and Machine Learning
by Georgina Faura, Gerard Boix-Lemonche, Anne Kristin Holmeide, Rasa Verkauskiene, Vallo Volke, Jelizaveta Sokolovska and Goran Petrovski
Sensors 2022, 22(3), 718; https://0-doi-org.brum.beds.ac.uk/10.3390/s22030718 - 18 Jan 2022
Cited by 7 | Viewed by 3623
Abstract
In this review, a selection of works on the sensing of biomarkers related to diabetes mellitus (DM) and diabetic retinopathy (DR) are presented, with the scope of helping and encouraging researchers to design sensor-array machine-learning (ML)-supported devices for robust, fast, and cost-effective early [...] Read more.
In this review, a selection of works on the sensing of biomarkers related to diabetes mellitus (DM) and diabetic retinopathy (DR) are presented, with the scope of helping and encouraging researchers to design sensor-array machine-learning (ML)-supported devices for robust, fast, and cost-effective early detection of these devastating diseases. First, we highlight the social relevance of developing systematic screening programs for such diseases and how sensor-arrays and ML approaches could ease their early diagnosis. Then, we present diverse works related to the colorimetric and electrochemical sensing of biomarkers related to DM and DR with non-invasive sampling (e.g., urine, saliva, breath, tears, and sweat samples), with a special mention to some already-existing sensor arrays and ML approaches. We finally highlight the great potential of the latter approaches for the fast and reliable early diagnosis of DM and DR. Full article
(This article belongs to the Special Issue Glucose Sensors and Artificial Intelligence)
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