This book covers the medical condition of diabetic patients, their early symptoms and methods conventionally used for diagnosing and monitoring diabetes. It describes various techniques and technologies used for diabetes detection. The content is built upon moving from regressive technology (invasive) and adapting new-age pain-free technologies (non-invasive), machine learning and artificial intelligence for diabetes monitoring and management. This book details all the popular technologies used in the health care and medical fields for diabetic patients. An entire chapter is dedicated to how the future of this field will be shaping up and the challenges remaining to be conquered. Finally, it shows artificial intelligence and predictions, which can be beneficial for the early detection, dose monitoring and surveillance for patients suffering from diabetes
Author(s): Kishor Kumar Sadasivuni, John-John Cabibihan, Abdulaziz Khalid A. M. Al-Ali, Rayaz A. Malik
Series: Springer Series on Bio- and Neurosystems, 13
Publisher: Springer
Year: 2022
Language: English
Pages: 312
City: Cham
Preface
Contents
Introduction
References
Review of Emerging Approaches Utilizing Alternative Physiological Human Body Fluids in Non- or Minimally Invasive Glucose Monitoring
1 Introduction
2 Alternative Physiological Body Fluids to Blood
3 Emerging Non- or Minimally Invasive Glucose Monitoring Techniques
4 Conclusions
5 Future Trends
References
Current Status of Non-invasive Diabetes Monitoring
1 Introduction
2 NIR Spectroscopy
3 Raman Spectroscopy
4 Bio-impedance Spectroscopy
5 Thermal Emission Spectroscopy
6 Optical Polarimetry
7 Fluorescence
8 Conclusion
References
A New Solution for Non-invasive Glucose Measurement Based on Heart Rate Variability
1 Introduction
2 State-of-the-Art Solutions
3 System Description
3.1 Functional Description
3.2 Technological Concept
4 Discussion
5 Conclusion
References
Optic Based Techniques for Monitoring Diabetics
1 Introduction
2 SPR Method for Detection of Diabetic Biomarkers
2.1 Glucose
2.2 Insulin
2.3 Glycated Hemoglobin (HbA1c)
2.4 Glutamic Acid Decarboxylase (GAD)
2.5 Acetone Vapor
3 SPR Imaging (SPRi)
4 Localized SPR (LSPR)
5 Photonic Crystals (PCs)
5.1 Brief Overview of PCs Physics
5.2 PCs Biosensors
6 Conclusion
References
SPR Assisted Diabetes Detection
1 Introduction
2 Experimental Analysis
2.1 Modification of SPR Chips
2.2 Preparation of SPR Nanofilms
2.3 Analysis of SPR Chips
2.4 Kinetic Studies
2.5 Artificial Plasma Studies
2.6 Site-Directed Mutagenesis
2.7 Protein Purification
2.8 Thiol Coupling of Proteins on CM5 Surfaces
2.9 Amine Coupling of Proteins on CM5 Surfaces
2.10 Ligand Injections
2.11 Regeneration
2.12 Denaturation of E149C GGBP Surface
3 Results and Discussions
3.1 Surface Plasmon Resonance (SPR)
3.2 Working Principle of SPR
3.3 BIO-RAD™ ProteOn 360 SPR Biosensor
3.4 Steps Involved in SPR
3.5 Analyzing Kinetics on a SPR Biosensor
3.6 Characterization of SPR Chips
3.7 Kinetic and Isotherm Analysis
3.8 Equilibrium Analysis and Association Kinetic Analysis
3.9 GGBP-Glucose SPR Signal
3.10 Comparison of GGBP Mutants
3.11 GGBP-Glucose Equilibrium-Binding Constant
3.12 GGBP Mutant Specificities to Other Carbohydrates
4 Conclusion and Future Outlook
References
Infrared and Raman Spectroscopy Assisted Diagnosis of Diabetics
1 Introduction
2 Raman Spectroscopy
2.1 Monitoring Glycated Hemoglobin (HbA1c) Levels for Indicating Diabetes
2.2 Monitoring Blood Glucose Levels for Indicating Diabetes
2.3 Monitoring Novel Biomarkers for Indicating Diabetes
2.4 General Application of Chemometric Methods for Indicating Diabetes Within Various Biological Samples
3 Infrared Spectroscopy
3.1 Monitoring Blood and Saliva Glucose Levels for Indicating Diabetes
3.2 Monitoring Novel Biomarkers for Indicating Diabetes
3.3 General Application of Chemometric Methods for Indicating Diabetes Within Various Biological Samples
4 Critical Evaluation
References
Photoacoustic Spectroscopy Mediated Non-invasive Detection of Diabetics
1 Introduction
2 History of Photoacoustic Spectroscopy
3 Conventional Methods of Glucose Monitoring
3.1 Invasive Methods of Glucose Monitoring
3.2 Minimally Invasive and Non-invasive Methods of Glucose Monitoring
4 Theory of Photoacoustic Spectroscopy
5 Recent Advancement in Photoacoustic Spectroscopy for the Detection of Glucose
6 Advantages of Photoacoustic Spectroscopy
7 Disadvantages of Photoacoustic Spectroscopy
8 Future Outlook
9 Conclusion
References
Electrical Bioimpedance Based Estimation of Diabetics
1 Electrical Bioimpedance: Physical Concepts
2 Basic Hardware Structures
2.1 Current Excitation Circuit
2.2 Voltage and Current Meters
3 Extracting Glucose from BIA
4 New Trends for Diabetic's Meter
5 Conclusion
References
Millimeter and Microwave Sensing Techniques for Diagnosis of Diabetes
1 Introduction
1.1 Overview
1.2 Types of Diabetes
2 Diagnosis of Diabetes
2.1 Testing Methodologies
3 Millimeter and Microwave Techniques for Sensing
3.1 Background: Mechanism of Millimeter and Microwave Techniques
4 Advantages and Disadvantages
5 Future Scope
6 Conclusion
References
Different Machine Learning Algorithms Involved in Glucose Monitoring to Prevent Diabetes Complications and Enhanced Diabetes Mellitus Management
1 Introduction
2 The Role of ML Algorithms in DM Management
2.1 BG Levels Prediction
2.2 Detection of DM-Associated Complications
3 Different Machine Learning Algorithms
3.1 Artificial Neural Network (ANN)
3.2 Support Vector Machines (SVM) and Gaussian Process Regression
3.3 Decision Tree and Random Forest
3.4 Logistic Regression
4 An Example of the Application of ML Algorithms Predicting BG Levels in Pregnant Women with GDM in Resource-Limited Regions
5 Outlook
6 Conclusion
References
The Role of Artificial Intelligence in Diabetes Management
1 Introduction
2 Related Work
3 Artificial Intelligence and Diabetes
4 Initiatives that Solve Diabetes Using Artificial Intelligence Techniques
5 Use Case: Prediction of Glycemic Using Artificial Intelligence Techniques
6 Conclusion
References
Artificial Intelligence and Machine Learning for Diabetes Decision Support
1 Introduction
2 Needs of the Patients
2.1 Prevention and Prognosis
2.2 Medication Management
2.3 Warning About Future Adversities
2.4 Risk Stratification
2.5 Personalization
2.6 Diabetes Education
2.7 Behavioral and Life Style Adjustments
3 Clinical DSS: Demands of Healthcare Professionals
3.1 Patient Safety
3.2 Diagnostic Support
3.3 Implementing Clinical Guidelines
4 What Can AI and ML Offer?
4.1 Detection/Description
4.2 Prediction
4.3 Recommendation
4.4 Clustering
5 Challenges for the Designers
6 Conclusion
References
Commercial Non-invasive Glucose Sensor Devices for Monitoring Diabetes
1 Introduction
2 Noninvasive Glucose Monitoring Care and Device Standards
2.1 Continuous Glucose Monitoring
2.2 Noninvasive Definition
2.3 Medical Device Definition
2.4 Accuracy Standards
3 Types of Noninvasive Glucose Biosensors
4 Principles of Noninvasive Glucose Monitoring
5 Platforms for Noninvasive Glucose Monitoring
6 Medical Device Regulation Updates
7 Future Outlook
8 Conclusion
References
Future Developments in Invasive and Non-invasive Diabetes Monitoring
1 Introduction
2 Diabetes Monitoring with Glucose Sensors
2.1 Description
2.2 Suitable Body Fluids Used for Glucose Monitoring Levels
2.3 Sensing Techniques for Glucose Detection
3 Commercial Non-invasive Glucose Meters
3.1 Glucowatch®
3.2 GlucoTrack™
3.3 Abbot FreeStyle® Libre
3.4 Medtronic Guardian™
4 Sensors in Development
5 Glucose Monitoring Informatics (GMI)
6 Tools and Standards for Assessing Accuracy
7 Conclusion
References
