Augmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligence

1st Edition - February 23, 2022
Editors: Anitha S. Pillai, Bindu Menon
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 9 0 0 3 7 - 9
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 8 8 6 2 6 - 0

Augmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligence focuses on how the neurosciences can benefit from advances in AI, especially in areas… Read more

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Augmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligence focuses on how the neurosciences can benefit from advances in AI, especially in areas such as medical image analysis for the improved diagnosis of Alzheimer’s disease, early detection of acute neurologic events, prediction of stroke, medical image segmentation for quantitative evaluation of neuroanatomy and vasculature, diagnosis of Alzheimer’s Disease, autism spectrum disorder, and other key neurological disorders. Chapters also focus on how AI can help in predicting stroke recovery, and the use of Machine Learning and AI in personalizing stroke rehabilitation therapy.

Other sections delve into Epilepsy and the use of Machine Learning techniques to detect epileptogenic lesions on MRIs and how to understand neural networks.

Cover image
Title page
Table of Contents
Copyright
List of contributors
1. Intracranial hemorrhage detection and classification using deep learning
Abstract
1.1 Introduction
1.2 Types of intracranial hemorrhage
1.3 Related work
1.4 Characteristic challenges of intracranial hemorrhage detection from computerized tomography images
1.5 Our approach
1.6 Conclusion
References
2. Deep learning for noninvasive management of brain tumors
Abstract
2.1 Introduction
2.2 Related works
2.3 Foundation of deep convolutional neural networks
2.4 Brain tumors and multimodal magnetic resonance imaging
2.5 Multiplanar convolutional neural networks for volumetric brain tumor segmentation
2.6 Deep radiomic for brain tumor classification
2.7 Experimental results
2.8 Conclusion
References
3. Artificial intelligence in Parkinson’s disease—symptoms identification and monitoring
Abstract
3.1 Introduction
3.2 Materials and methods
3.3 Discussion
References
4. Alzheimer’s disease detection using artificial intelligence
Abstract
4.1 Introduction
4.2 Background/literature review
4.3 Classification methods for alzheimer’s disease detection
4.4 Alzheimer’s disease detection using artificial intelligence
4.5 Discussion
4.6 Conclusion
References
5. Intelligent computer systems for multiple sclerosis diagnosis
Abstract
5.1 Introduction
5.2 A review of reasoning methods in intelligent systems for MS diagnosis
5.3 Rule-based CDSS for MS (relapsing-remitting) diagnosis
5.4 Fuzzy rule-based CDSS for MS (relapsing-remitting) diagnosis
5.5 Conclusion
References
6. Current and future applications of artificial intelligence in multiple sclerosis
Abstract
6.1 Introduction
6.2 Artificial intelligence techniques in multiple sclerosis: machine learning and deep learning
6.3 Artificial intelligence on magnetic resonance imaging
6.4 Artificial intelligence on other measures
6.5 Clinical applications
6.6 Future development
6.7 Conclusion
References
7. Artificial intelligence–assisted headache classification: a review
Abstract
7.1 Introduction
7.2 AI-based techniques for diagnosis, classification, and management of headache disorders
7.3 Major drawbacks and challenges
7.4 Conclusion
References
8. Deep learning for reliable detection of epileptogenic lesions
Abstract
8.1 Background
8.2 Deep learning: a brief introduction
8.3 Deep learning is well-suited to detect common brain lesions
8.4 The challenge: how to detect rare brain lesions with deep learning?
8.5 Explainable and interpretable deep learning: a view into the black box
8.6 Generalizability of deep learning models
8.7 Conclusion
Acknowledgments
References
9. Artificial intelligence in neurosciences—are we really there?
Abstract
9.1 Introduction
9.2 Exponential increase in healthcare artificial intelligence publications
9.3 Components of artificial intelligence
9.4 Overview of artificial intelligence in clinical neurosciences
9.5 Challenges in artificial intelligence implementation
9.6 Financial implications
9.7 Trust
9.8 Ethical challenges
9.9 Regulatory issues
9.10 Legal issues
9.11 Neuroscience and artificial intelligence
9.12 Conclusion
Acknowledgment
References
10. Artificial intelligence in the management of neurological disorders: its prevalence and prominence
Abstract
10.1 Introduction
10.2 Overview of artificial, machine learning, and deep learning in healthcare
10.3 Type of data used by artificial intelligence in neurology
10.4 Artificial intelligence in neurology
10.5 Discussion: findings and open issues
10.6 Conclusion
References
11. Graphical assessment of the internal structure of Parkinsons dataset—a case study
Abstract
11.1 Introduction to multivariate data analysis
11.2 The data on Parkinsons disease
11.3 The biplot methodology
11.4 Analyzing Parkinson’s disease data using biplots
11.5 Selection of the classifier
11.6 Discussion and closing remarks
References
12. Applications of artificial intelligence to neurological disorders: current technologies and open problems
Abstract
12.1 Introduction
12.2 Neurological disorders
12.3 Artificial intelligence/machine learning algorithms
12.4 Conclusion
12.5 Abbreviations
References
13. Developing a chatbot/intelligent system for neurological diagnosis and management
Abstract
13.1 Introduction
13.2 History of chatbot/intelligent system use in neurology
13.3 Different modalities of chatbot/intelligent systems in neurological diagnosis
13.4 Where is the intelligence in a chatbot/intelligent systems?
13.5 Designing a chatbot for a neurological condition
13.6 Limitations of chatbots and intelligent systems
13.7 Ethical and legal issues
13.8 Future directions
Acknowledgment
References
14. Artificial intelligence in the diagnosis and management of acute ischemic stroke
Abstract
14.1 Introduction
14.2 Different artificial intelligence types and their implication on stroke triage and management
14.3 Use of artificial intelligence in acute ischemic stroke
14.4 Challenges in use of artificial intelligence in stroke care
14.5 Using artificial intelligence in acute ischemic stroke—author experience
14.6 Conclusion and the way forward
References
15. Towards intelligent extended reality in stroke rehabilitation: Application of machine learning and artificial intelligence in rehabilitation
Abstract
15.1 Introduction
15.2 Current methods of stroke rehabilitation
15.3 Current extended reality technologies in stroke rehabilitation
15.4 Strategies to improve immersion and presence
15.5 Conclusions and future direction
References
Index

Anitha S. Pillai

Dr. Anitha S. Pillai is a Professor in the School of Computing Sciences, Hindustan University, Chennai, India. She has 26 years of teaching and research experience. Her main areas of research are Artificial intelligence, Machine Learning, Natural Language Processing and Healthcare Analytics. She has authored/co-authored more than 90 papers in international journals and book chapters. She is the founder of AtINeu http://atineu.org/ Research Labs, which focusses on the use of Machine Learning/Deep Learning, Virtual Reality, and Augmented Reality in Healthcare. Dr. Pillai is also the co-editor of the book Virtual and Augmented Reality in Education, Art and Museums published by IGI Global,USA and Extended Reality Usage during COVID 19 Pandemic published by Springer Nature, Switzerland.

Affiliations and expertise

Professor, School of Computing Sciences, Hindustan University, Chennai, India

Bindu Menon

Dr. Bindu Menon (M.D. (Med.), D.M. (Neuro), DNB. (Neuro), PGDCN (Neuro),(London), FRCP (Edinburgh), MNAMS, FICP, FIAN) is the Head of Department and Senior Consultant Neurologist, Apollo Specialty Hospitals, Nellore, India. She has 18 years of teaching experience of 18 years is is on the Editorial Board of the International Journal of Epilepsy. Dr. Menon is a research committee member of the World Stroke Organization, Editor-in-Chief of Epilepsy India. She has been the recipient of 18 international and national awards for epilepsy and stroke care, which includes the prestigious International League Against Epilepsy (ILAE) Leadership Program Award in Barcelona, 2017, the World Stroke Day Award under the Individual Achievement category from the World Stroke Organization, in Montreal, 2018, among others. Dr. Menon is a Fellow of the Geriatric Society of India and has received the honorary title of Distinguished Clinical Tutor. In August 2013, she founded the Dr. Bindu Menon Foundation, and has been instrumental in starting several novel treatment approaches for epilepsy patients, including Neurology on Wheels and an app titled Epilepsy Help.

Affiliations and expertise

Head of Department and Senior Consultant Neurologist, Apollo Specialty Hospitals, Nellore, India

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

Health