Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors
Wearable continuous glucose monitoring (CGM) sensors are revolutionizing the treatment of type 1 diabetes (T1D). These sensors provide in real-time, every 1–5 min, the current blood glucose concentration and its rate-of-change, two key pieces of information for improving the determination of exogeno...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/14/3870 |
| _version_ | 1797562746206683136 |
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| author | Martina Vettoretti Giacomo Cappon Andrea Facchinetti Giovanni Sparacino |
| author_facet | Martina Vettoretti Giacomo Cappon Andrea Facchinetti Giovanni Sparacino |
| author_sort | Martina Vettoretti |
| collection | DOAJ |
| description | Wearable continuous glucose monitoring (CGM) sensors are revolutionizing the treatment of type 1 diabetes (T1D). These sensors provide in real-time, every 1–5 min, the current blood glucose concentration and its rate-of-change, two key pieces of information for improving the determination of exogenous insulin administration and the prediction of forthcoming adverse events, such as hypo-/hyper-glycemia. The current research in diabetes technology is putting considerable effort into developing decision support systems for patient use, which automatically analyze the patient’s data collected by CGM sensors and other portable devices, as well as providing personalized recommendations about therapy adjustments to patients. Due to the large amount of data collected by patients with T1D and their variety, artificial intelligence (AI) techniques are increasingly being adopted in these decision support systems. In this paper, we review the state-of-the-art methodologies using AI and CGM sensors for decision support in advanced T1D management, including techniques for personalized insulin bolus calculation, adaptive tuning of bolus calculator parameters and glucose prediction. |
| first_indexed | 2024-03-10T18:32:30Z |
| format | Article |
| id | doaj.art-5020b4b0bba644a39684bc66cbc19052 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T18:32:30Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5020b4b0bba644a39684bc66cbc190522023-11-20T06:27:56ZengMDPI AGSensors1424-82202020-07-012014387010.3390/s20143870Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring SensorsMartina Vettoretti0Giacomo Cappon1Andrea Facchinetti2Giovanni Sparacino3Department of Information Engineering, University of Padova, 35131 Padova, ItalyDepartment of Information Engineering, University of Padova, 35131 Padova, ItalyDepartment of Information Engineering, University of Padova, 35131 Padova, ItalyDepartment of Information Engineering, University of Padova, 35131 Padova, ItalyWearable continuous glucose monitoring (CGM) sensors are revolutionizing the treatment of type 1 diabetes (T1D). These sensors provide in real-time, every 1–5 min, the current blood glucose concentration and its rate-of-change, two key pieces of information for improving the determination of exogenous insulin administration and the prediction of forthcoming adverse events, such as hypo-/hyper-glycemia. The current research in diabetes technology is putting considerable effort into developing decision support systems for patient use, which automatically analyze the patient’s data collected by CGM sensors and other portable devices, as well as providing personalized recommendations about therapy adjustments to patients. Due to the large amount of data collected by patients with T1D and their variety, artificial intelligence (AI) techniques are increasingly being adopted in these decision support systems. In this paper, we review the state-of-the-art methodologies using AI and CGM sensors for decision support in advanced T1D management, including techniques for personalized insulin bolus calculation, adaptive tuning of bolus calculator parameters and glucose prediction.https://www.mdpi.com/1424-8220/20/14/3870continuous glucose monitoring sensorartificial intelligencedecision support systempredictionoptimizationpersonalized therapy |
| spellingShingle | Martina Vettoretti Giacomo Cappon Andrea Facchinetti Giovanni Sparacino Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors Sensors continuous glucose monitoring sensor artificial intelligence decision support system prediction optimization personalized therapy |
| title | Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors |
| title_full | Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors |
| title_fullStr | Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors |
| title_full_unstemmed | Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors |
| title_short | Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors |
| title_sort | advanced diabetes management using artificial intelligence and continuous glucose monitoring sensors |
| topic | continuous glucose monitoring sensor artificial intelligence decision support system prediction optimization personalized therapy |
| url | https://www.mdpi.com/1424-8220/20/14/3870 |
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