Augmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligence

Augmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligence

1st Edition - February 23, 2022

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  • Editors: Anitha Pillai, Bindu Menon
  • Paperback ISBN: 9780323900379
  • eBook ISBN: 9780323886260

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Description

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.

Key Features

  • Provides readers with an understanding on the key applications of artificial intelligence and machine learning in the diagnosis and treatment of the most important neurological disorders
  • Integrates recent advancements of artificial intelligence and machine learning to the evaluation of large amounts of clinical data for the early detection of disorders such as Alzheimer’s Disease, autism spectrum disorder, Multiple Sclerosis, headache disorder, Epilepsy, and stroke
  • Provides readers with illustrative examples of how artificial intelligence can be applied to outcome prediction, neurorehabilitation and clinical exams, including a wide range of case studies in predicting and classifying neurological disorders

Readership

Academics (scientists, researchers, MSc. PhD. students) from the fields of Computer Science, Artificial Intelligence, Neural Engineering, and Information Technology. The audience also includes Neurologists who are interested in the diagnostic and treatment aspects

Table of Contents

  • 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

Product details

  • No. of pages: 362
  • Language: English
  • Copyright: © Academic Press 2022
  • Published: February 23, 2022
  • Imprint: Academic Press
  • Paperback ISBN: 9780323900379
  • eBook ISBN: 9780323886260

About the Editors

Anitha 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

Ratings and Reviews

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  • Priya Mon Feb 21 2022

    Excellent Book

    It is an excellent book for the AI community and neurologists