Main description:

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

Contents:

S.No

Chapter Title

Tentative authors

Email

Affliation

1

Diabetics, Classification of Diagnosis Methods and Accuracy Assessment Standards:

Lutz Heinemann

l.heinemann@science-co.com

Science Consulting in Diabetes GmbH, 40468 Dusseldorf, Germany

2

Conventional Methods for Diabetics Monitoring

MarcusLind

lind.marcus@telia.com

Diabetes Outpatient Clinic, Uddevalla Hospital, 451 80 Uddevalla, Sweden

3

Optics Based Techniques for Monitoring Diabetics

Ishan Barman

ibarman@jhu.edu

Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA

4

Surface Plasmon Resonance (SPR) Assisted Diabetics Detection

Jean-Francois Masson

jf.masson@umontreal.ca

Centre for self-assembled chemical structures (CSACS), McGill University, 801 Sherbrooke Street West, Montreal, Quebec H3A 2K6, Canada

5

Role of Fluorescence Technology in Diagnosis of Diabetics

Jin Zhang

jzhang@eng.uwo.ca

Biomedical Engineering Graduate Program, University of Western Ontario, 1151 Richmond St., London, ON N6A 5B9, Canada

6

Infrared and Raman Spectroscopy Assisted Diagnosis of Diabetics

Zhengjun ZhangKey

zjzhang@tsinghua.edu.cn

Laboratory of Advanced Materials (MOE), School of Materials Science and Engineering, Tsinghua University, Beijing, P.R. China

7

Minimally-Invasive and Non-Invasive Technologies: An Overview

Wilbert Villena Gonzales

w.villena@uq.edu.au

School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia,Brisbane 4072, Australia

8

Photoacoustic Spectroscopy Mediated Non-Invasive Detection of Diabetics

Mioara PETRUS

mioara.petrus@inflpr.ro

Department of Lasers, National Institute for Laser, Plasma, and Radiation Physics, 409 Atomistilor St., PO Box MG-36, 077125 Bucharest, Roumania

9

Bioimpedance Spectroscopy Based Estimation of Diabetics

Anja Schork

Anja.Schork@med.uni-tuebingen.de

Department of Internal Medicine IV, Division of Endocrinology,Diabetology, Vascular Disease, Nephrology and Clinical Chemistry,University Hospital Tubingen, Otfried-Muller-Str.10, 72076 Tubingen,Germany

10

Millimeter and Microwave Sensing Technique for Diagnosis of Diabetics

Ala Eldin Omer

aeomomer@uwaterloo.ca

Centre for Intelligent Antenna and Radio Systems (CIARS), Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada

11

Indicating Diabetics Level by Non-Invasive Electromagnetic Sensing Technique

Yuanjin Zheng

yjzheng@ntu.edu.sg

School of Electrical and Electronic Engineering, Nanyang Technological University,Singapore 639798, Singapore

12

Metabolic Heat Conformation Based Non-Invasive Monitoring of Diabetics

Yu Huang

yu-huang@cuhk.edu.hk

School of Biomedical Sciences, Chinese University of Hong Kong, Hong Kong

13

Current Status of Invasive Diabetics Monitoring

Andrew J. Flewitt

ajf@eng.cam.ac.uk

Electrical Engineering Division, Department of Engineering, University of Cambridge, J J Thomson Avenue,Cambridge CB3 0FA, UK

14

Commercial Non-Invasive Devices for Diabetics Monitoring

Maryamsadat Shokrekhodaei

mshokrekhod@miners.utep.edu

Department of Electrical and Computer Engineering, The University of Texas at El Paso,El Paso, TX 79968, USA

15

Future Developments in Invasive and Non-Invasive Diabetics Monitoring

Ronny Priefer

ronny.priefer@mcphs.edu

Massachusetts College of Pharmacy and Health Sciences University, Boston, MA, USA

16

Different Machine Learning Algorithm involved in Glucose Monitoring to Prevent Diabetes Complications

and Enhanced Diabetes Mellitus Management

Rekha Phadke

rekhaphadke@gmail.com

Department of Electronics and Communication, NMIT, Bangalore, India

17

The role of Artificial Intelligence in Diabetes management

Jyotismita Chaki

jyotismita.c@gmail.com

School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India

18

Artificial Intelligence and Machine learning for Diabetes Decision Support

Josep Vehi

josep.vehi@udg.edu

POLITECNICA IV
Campus Montilivi
17003 - GIRONA
Despatx: 131