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Submit a ProposalArtificial Intelligence in Neurological Disorders
 | Management, Diagnosis and Treatment Edited by Rishabha Malviya, Suraj Kumar, Aditya Sushil Solanke, Priyanshi Goyal, Kapil Chauhan Copyright: 2025 | Expected Pub Date:2025/06/30 ISBN: 9781394347506 | Hardcover | 260 pages |
One Line Description
The book gives invaluable insights into how artificial intelligence is revolutionizing
the management and treatment of neurological disorders, empowering you
to stay ahead in the rapidly evolving landscape of healthcare.
Audience
Researchers, scientists, industrialists, faculty members, healthcare professionals, hospital management, biomedical industrialists, engineers, and IT professionals interested in studying the intersection of AI and neurology
Researchers, scientists, industrialists, faculty members, healthcare professionals, hospital management, biomedical industrialists, engineers, and IT professionals interested in studying the intersection of AI and neurology
Description
Embark on a groundbreaking exploration of the intersection between cutting-edge technology and the intricate complexities of neurological disorders. Artificial Intelligence in Neurological Disorder: Management, Diagnosis, and Treatment comprehensively introduces how artificial intelligence is becoming a vital ally in neurology, offering unprecedented advancements in management, diagnosis, and treatment. As the digital age converges with medical expertise, this book unveils a comprehensive roadmap for leveraging artificial intelligence to revolutionize neurological healthcare. Delve into the core principles that underpin AI applications in the field by exploring intricate algorithms that enhance the precision of diagnosis and how machine learning not only refines the understanding of neurological disorders but also paves the way for personalized treatment strategies tailored to individual patient needs. With compelling case studies and real-world examples, the realms of neuroscience and artificial intelligence converge, illustrating the symbiotic relationship that holds the promise of transforming patient care.
Readers of this book will find it:
• Provides future perspectives on advancing artificial intelligence applications in neurological disorders;
• Focuses on the role of AI in diagnostics, delving into how advanced algorithms and machine learning techniques contribute to more accurate and timely diagnosis of neurological disorders;
• Emphasizes practical integration of AI tools into clinical practice, offering insights into how healthcare professionals can leverage AI technology for more effective patient care;
• Recognizes the interdisciplinary nature of neurology and AI, bridging the gap between these fields, making it accessible to healthcare professionals, researchers, and technologists;
• Addresses the ethical implications of AI in healthcare, exploring issues such as data privacy, bias, and the responsible deployment of AI technologies in the neurological domain.
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Embark on a groundbreaking exploration of the intersection between cutting-edge technology and the intricate complexities of neurological disorders. Artificial Intelligence in Neurological Disorder: Management, Diagnosis, and Treatment comprehensively introduces how artificial intelligence is becoming a vital ally in neurology, offering unprecedented advancements in management, diagnosis, and treatment. As the digital age converges with medical expertise, this book unveils a comprehensive roadmap for leveraging artificial intelligence to revolutionize neurological healthcare. Delve into the core principles that underpin AI applications in the field by exploring intricate algorithms that enhance the precision of diagnosis and how machine learning not only refines the understanding of neurological disorders but also paves the way for personalized treatment strategies tailored to individual patient needs. With compelling case studies and real-world examples, the realms of neuroscience and artificial intelligence converge, illustrating the symbiotic relationship that holds the promise of transforming patient care.
Readers of this book will find it:
• Provides future perspectives on advancing artificial intelligence applications in neurological disorders;
• Focuses on the role of AI in diagnostics, delving into how advanced algorithms and machine learning techniques contribute to more accurate and timely diagnosis of neurological disorders;
• Emphasizes practical integration of AI tools into clinical practice, offering insights into how healthcare professionals can leverage AI technology for more effective patient care;
• Recognizes the interdisciplinary nature of neurology and AI, bridging the gap between these fields, making it accessible to healthcare professionals, researchers, and technologists;
• Addresses the ethical implications of AI in healthcare, exploring issues such as data privacy, bias, and the responsible deployment of AI technologies in the neurological domain.
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Author / Editor Details
Rishabha Malviya, PhD is an associate professor in the Department of Pharmacy in the School of Medical and Allied Services at Galgotias University with over 13 years of research experience. He has authored 57 books, 58 chapters, and over 150 research papers for national and international journals of repute, as well as 51 patents. His areas of interest include formulation optimization, nanoformulation, targeted drug delivery, localized drug delivery, and characterization of natural polymers as pharmaceutical excipients.
Rishabha Malviya, PhD is an associate professor in the Department of Pharmacy in the School of Medical and Allied Services at Galgotias University with over 13 years of research experience. He has authored 57 books, 58 chapters, and over 150 research papers for national and international journals of repute, as well as 51 patents. His areas of interest include formulation optimization, nanoformulation, targeted drug delivery, localized drug delivery, and characterization of natural polymers as pharmaceutical excipients.
Suraj Kumar is an assistant professor in the School of Medical and Allied Sciences at Galgotias University. He has published over ten papers in international journals and five book chapters. His research interests include sustainable polymeric fibers, nanoparticles, and controlled drug delivery.
Aditya Sushil Solanke, PhD is a Senior Resident in Neurosurgery at Byramjee Jeejeebhoy Government Medical College and Sassoon Hospital, India. He completed his Bachelor of Medicine, Bachelor of Surgery, and Masters in General Surgery from the Government Medical College in Nagpur.
Priyanshi Goyal, M.Pharm is an assistant professor in the School of Pharmacy at Mangalayatan University. She has authored seven review articles and two books and attended 14 national and international conferences and webinars. Her area of interest is treatment strategies for neurological disorders.
Kapil Chauhan, PhD is an emergency physician at Max Hospital in Dehradun, India. He completed his Bachelor of Medicine and Bachelor of Surgery from Teerthanker Mahavir Medical College and Masters in Emergency Medicine from Max Hospital.
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Table of Contents
Foreword
Preface
1. Artificial Intelligence in Neuroscience: A Clinician’s Perception
1.1 Introduction
1.2 Artificial Intelligence and Healthcare
1.3 Prediction Model of AI
1.4 AI in Upskilling Neurosurgical Procedures
1.4.1 AI in Seizure Disorders
1.4.2 AI in the Neurosurgical OT
1.4.3 AI in Neuro-Oncology
1.4.4 Neurotrauma and Artificial Intelligence
1.4.5 Using AI for Medical Imaging
1.4.6 Analytical Intelligence for Stroke
1.4.7 The Use of Artificial Intelligence in Neurorehabilitation
1.5 Artificial-Intelligence-Enhanced Smart Gadgets
1.5.1 Smartphone and Sensor
1.5.2 Genetic and Genomic Data
1.5.3 Integration of Multimodal Data
1.6 The Use of Deep Learning to Glean Insights from Massive Datasets is Gaining Popularity
1.7 Multimodal Data Improves the Predictive Ability of AI Models
1.8 Conclusion
References
2. Application of AI to a Neurological Disorder
2.1 Introduction
2.2 Various Forms of AI and Existing Studies
2.2.1 Stroke
2.2.2 Epilepsy
2.2.3 Concussion
2.2.4 Dementia
2.2.5 Movement Disorder
2.3 Outcomes and Limitation
2.4 Future Direction
2.5 Conclusion
References
3. Treatment Strategies of Neurological Disorders with Deep Learning Algorithm
3.1 Introduction
3.2 Concept of Deep Learning
3.3 Literature Review
3.4 Deep Learning in Neurology
3.4.1 The Categorization of Medical Images
3.4.2 Segmentation of Medical Images
3.4.3 Disorder Categorization Based on Functional Connectivity in the Brain
3.4.4 Predicting Potential Dangers
3.5 Challenges
3.5.1 Data Volume
3.5.2 Data Quality
3.5.3 Generalizability
3.5.4 Interpretability
3.5.5 Legal
3.5.6 Ethical
3.6 Conclusion
References
4. Deep Learning for Early Diagnosis of Neurological Disorders
4.1 Introduction
4.2 Reconstructing and Cleaning Up Raw Data
4.2.1 Recreating an Image
4.2.2 Boosting the Signal
4.2.3 Cross-Modality Image Synthesis
4.3 Extraction of Biomarkers
4.3.1 Anatomical Structures are Divided Into Segments
4.3.2 Classification of Tumors
4.3.3 Brain Age
4.3.4 Electroencephalography Signal Biomarker Detection
4.4 Disease Detection and Diagnosis
4.4.1 Identifying Illness
4.4.2 Classification of Known Diseases
4.5 Disease Prediction
4.5.1 Time-Based Categorization
4.5.2 Regression of the Risk of Contracting a Disease
4.5.3 Forecasting Longitudinal Images
4.6 Advancing our Knowledge of Disease
4.6.1 Identification of New Disease Subtype
4.6.2 Disease Progression Modeling
4.6.3 Identification of Genetic Variants Associated with a Disease
4.7 Curing Ailments
4.7.1 Forecasting the Results of Treatment
4.7.2 Creating New Medicines
4.8 Future Tendencies
4.9 Conclusion
References
5. Diagnosis of Neurological Disorders Using Artificial Intelligence Advances
5.1 Introduction
5.2 Evolutionary Model for Generalized BCI Technologies
5.2.1 Classical Brain Interface
5.2.2 Brain–Computer Interaction
5.2.3 Brain–Computer Intelligence
5.3 BCI and AI
5.4 Challenges and Opportunities
5.4.1 Channel Capacity
5.4.2 The Brain–Computer Interface and Human Enhancement
5.5 Application of Radiology in Neurological Disorder
5.5.1 Tracking Abnormalities in the Nervous System
5.5.2 Brain Tumors and Their Classification
5.5.3 Radiomics Data Extraction
5.5.4 Innovative Methods for Identifying Cracks
5.6 Conclusion
References
6. Integrating Artificial Intelligence with Neuroimaging
6.1 Introduction
6.2 Classification and Regression of Deep Learning for Neuroimaging
6.2.1 Models Using Multi-Layer Perceptrons
6.2.2 Convolutional Networks on Graphs and Convolutional Neural Networks
6.2.3 Recurring Neuronal Systems
6.2.4 Generative Adversarial Network (GANs)
6.2.5 Attention Modules
6.3 Deep Learning Model
6.3.1 Feed-Forward Neural Networks
6.3.2 Stacking Auto-Encoders
6.3.3 Deep Belief Networks (DBN)
6.3.4 Deep Boltzmann Machines
6.4 Various DL to Mitigate the Peril of Image Acquisition
6.4.1 MRI
6.4.2 CT
6.4.3 Sparse-Data CT
6.4.4 PET
6.5 Applications for the Analysis of Brain Disorders Using Medical Images
6.5.1 Deep Learning for Alzheimer’s Disease Analysis
6.5.2 Deep Learning for Parkinson’s Disease Analysis
6.5.3 Deep Learning for Autism Spectrum Disorder Analysis
6.5.4 Deep Learning for Schizophrenia Analysis
6.6 Conclusion
References
7. Cognitive Therapy for Brain Disease: Using a Deep Learning Model
7.1 Introduction
7.2 Background
7.3 Related Work
7.4 Methods
7.4.1 Stacked Auto-Encoder (SAE)
7.4.2 Deep Boltzmann Machine (DBM)
7.5 CNN Model Identifies Phases of AD
7.5.1 Data Source
7.5.2 CNN-Based Approach
7.5.2.1 ReLU (Non-Linear Function)
7.5.2.2 Batch Normalization
7.5.2.3 Average Pooling
7.5.2.4 Fully Connected (FC)
7.5.2.5 Model Description
7.5.3 Training and Evaluation
7.6 Conclusion
References
8. AI Advancements in Tailored Healthcare for Neurodevelopmental Disorders
8.1 Introduction
8.2 Integration of Personalized Medicine and Artificial Intelligence
8.3 Neurodevelopmental Disorders (NDDs)
8.4 Artificial Intelligence in NDDs
8.5 Challenges for Artificial Intelligence about NDDs
8.6 Conclusion
References
9. Artificial Intelligence and Nanorobotic Application in Neurological Disorder
9.1 Introduction
9.2 Methods for the Determination of AI and Nanorobotic Application in Neurological Disorder
9.3 Artificial Intelligence Tools for Self-Driving Pharmaceutical Treatment
9.4 Telemedicine Tools that Can be Used at Dwelling
9.5 Robotics and Artificial Intelligence (AI) are Used to Manage and Control Human Walking Patterns
9.6 Conclusion
References
10. Insightful Vision: Exploring the Contemporary Applications of Artificial Intelligence in Ophthalmology
10.1 Introduction
10.2 Essential Elements of an Artificial Intelligence Platform
10.2.1 Machine Learning and Deep Learning Methodologies
10.2.2 Machine Learning and Deep Learning Methodologies in Ophthalmology
10.3 Utilizing Deep Learning-Based Artificial Intelligence to Forecast Visual Acuity Following Vitrectomy Surgery
10.4 Applications of Artificial Intelligence in Ophthalmology
10.4.1 Artificial Intelligence to Recognize Diabetic Retinopathy
10.4.2 Applications of AI in Recognizing Glaucoma
10.4.3 Applications of AI in Recognizing Age-Related Macular Degeneration (AMD)
10.4.4 Applications of AI in Recognizing Neural Networks in Cataract and Other Pathologies
10.5 Discussion and Perspectives
10.6 The Benefits and Constraints of Utilizing AI Tools in Ophthalmology
10.7 Conclusions
References
Index
Back to Top
Foreword
Preface
1. Artificial Intelligence in Neuroscience: A Clinician’s Perception
1.1 Introduction
1.2 Artificial Intelligence and Healthcare
1.3 Prediction Model of AI
1.4 AI in Upskilling Neurosurgical Procedures
1.4.1 AI in Seizure Disorders
1.4.2 AI in the Neurosurgical OT
1.4.3 AI in Neuro-Oncology
1.4.4 Neurotrauma and Artificial Intelligence
1.4.5 Using AI for Medical Imaging
1.4.6 Analytical Intelligence for Stroke
1.4.7 The Use of Artificial Intelligence in Neurorehabilitation
1.5 Artificial-Intelligence-Enhanced Smart Gadgets
1.5.1 Smartphone and Sensor
1.5.2 Genetic and Genomic Data
1.5.3 Integration of Multimodal Data
1.6 The Use of Deep Learning to Glean Insights from Massive Datasets is Gaining Popularity
1.7 Multimodal Data Improves the Predictive Ability of AI Models
1.8 Conclusion
References
2. Application of AI to a Neurological Disorder
2.1 Introduction
2.2 Various Forms of AI and Existing Studies
2.2.1 Stroke
2.2.2 Epilepsy
2.2.3 Concussion
2.2.4 Dementia
2.2.5 Movement Disorder
2.3 Outcomes and Limitation
2.4 Future Direction
2.5 Conclusion
References
3. Treatment Strategies of Neurological Disorders with Deep Learning Algorithm
3.1 Introduction
3.2 Concept of Deep Learning
3.3 Literature Review
3.4 Deep Learning in Neurology
3.4.1 The Categorization of Medical Images
3.4.2 Segmentation of Medical Images
3.4.3 Disorder Categorization Based on Functional Connectivity in the Brain
3.4.4 Predicting Potential Dangers
3.5 Challenges
3.5.1 Data Volume
3.5.2 Data Quality
3.5.3 Generalizability
3.5.4 Interpretability
3.5.5 Legal
3.5.6 Ethical
3.6 Conclusion
References
4. Deep Learning for Early Diagnosis of Neurological Disorders
4.1 Introduction
4.2 Reconstructing and Cleaning Up Raw Data
4.2.1 Recreating an Image
4.2.2 Boosting the Signal
4.2.3 Cross-Modality Image Synthesis
4.3 Extraction of Biomarkers
4.3.1 Anatomical Structures are Divided Into Segments
4.3.2 Classification of Tumors
4.3.3 Brain Age
4.3.4 Electroencephalography Signal Biomarker Detection
4.4 Disease Detection and Diagnosis
4.4.1 Identifying Illness
4.4.2 Classification of Known Diseases
4.5 Disease Prediction
4.5.1 Time-Based Categorization
4.5.2 Regression of the Risk of Contracting a Disease
4.5.3 Forecasting Longitudinal Images
4.6 Advancing our Knowledge of Disease
4.6.1 Identification of New Disease Subtype
4.6.2 Disease Progression Modeling
4.6.3 Identification of Genetic Variants Associated with a Disease
4.7 Curing Ailments
4.7.1 Forecasting the Results of Treatment
4.7.2 Creating New Medicines
4.8 Future Tendencies
4.9 Conclusion
References
5. Diagnosis of Neurological Disorders Using Artificial Intelligence Advances
5.1 Introduction
5.2 Evolutionary Model for Generalized BCI Technologies
5.2.1 Classical Brain Interface
5.2.2 Brain–Computer Interaction
5.2.3 Brain–Computer Intelligence
5.3 BCI and AI
5.4 Challenges and Opportunities
5.4.1 Channel Capacity
5.4.2 The Brain–Computer Interface and Human Enhancement
5.5 Application of Radiology in Neurological Disorder
5.5.1 Tracking Abnormalities in the Nervous System
5.5.2 Brain Tumors and Their Classification
5.5.3 Radiomics Data Extraction
5.5.4 Innovative Methods for Identifying Cracks
5.6 Conclusion
References
6. Integrating Artificial Intelligence with Neuroimaging
6.1 Introduction
6.2 Classification and Regression of Deep Learning for Neuroimaging
6.2.1 Models Using Multi-Layer Perceptrons
6.2.2 Convolutional Networks on Graphs and Convolutional Neural Networks
6.2.3 Recurring Neuronal Systems
6.2.4 Generative Adversarial Network (GANs)
6.2.5 Attention Modules
6.3 Deep Learning Model
6.3.1 Feed-Forward Neural Networks
6.3.2 Stacking Auto-Encoders
6.3.3 Deep Belief Networks (DBN)
6.3.4 Deep Boltzmann Machines
6.4 Various DL to Mitigate the Peril of Image Acquisition
6.4.1 MRI
6.4.2 CT
6.4.3 Sparse-Data CT
6.4.4 PET
6.5 Applications for the Analysis of Brain Disorders Using Medical Images
6.5.1 Deep Learning for Alzheimer’s Disease Analysis
6.5.2 Deep Learning for Parkinson’s Disease Analysis
6.5.3 Deep Learning for Autism Spectrum Disorder Analysis
6.5.4 Deep Learning for Schizophrenia Analysis
6.6 Conclusion
References
7. Cognitive Therapy for Brain Disease: Using a Deep Learning Model
7.1 Introduction
7.2 Background
7.3 Related Work
7.4 Methods
7.4.1 Stacked Auto-Encoder (SAE)
7.4.2 Deep Boltzmann Machine (DBM)
7.5 CNN Model Identifies Phases of AD
7.5.1 Data Source
7.5.2 CNN-Based Approach
7.5.2.1 ReLU (Non-Linear Function)
7.5.2.2 Batch Normalization
7.5.2.3 Average Pooling
7.5.2.4 Fully Connected (FC)
7.5.2.5 Model Description
7.5.3 Training and Evaluation
7.6 Conclusion
References
8. AI Advancements in Tailored Healthcare for Neurodevelopmental Disorders
8.1 Introduction
8.2 Integration of Personalized Medicine and Artificial Intelligence
8.3 Neurodevelopmental Disorders (NDDs)
8.4 Artificial Intelligence in NDDs
8.5 Challenges for Artificial Intelligence about NDDs
8.6 Conclusion
References
9. Artificial Intelligence and Nanorobotic Application in Neurological Disorder
9.1 Introduction
9.2 Methods for the Determination of AI and Nanorobotic Application in Neurological Disorder
9.3 Artificial Intelligence Tools for Self-Driving Pharmaceutical Treatment
9.4 Telemedicine Tools that Can be Used at Dwelling
9.5 Robotics and Artificial Intelligence (AI) are Used to Manage and Control Human Walking Patterns
9.6 Conclusion
References
10. Insightful Vision: Exploring the Contemporary Applications of Artificial Intelligence in Ophthalmology
10.1 Introduction
10.2 Essential Elements of an Artificial Intelligence Platform
10.2.1 Machine Learning and Deep Learning Methodologies
10.2.2 Machine Learning and Deep Learning Methodologies in Ophthalmology
10.3 Utilizing Deep Learning-Based Artificial Intelligence to Forecast Visual Acuity Following Vitrectomy Surgery
10.4 Applications of Artificial Intelligence in Ophthalmology
10.4.1 Artificial Intelligence to Recognize Diabetic Retinopathy
10.4.2 Applications of AI in Recognizing Glaucoma
10.4.3 Applications of AI in Recognizing Age-Related Macular Degeneration (AMD)
10.4.4 Applications of AI in Recognizing Neural Networks in Cataract and Other Pathologies
10.5 Discussion and Perspectives
10.6 The Benefits and Constraints of Utilizing AI Tools in Ophthalmology
10.7 Conclusions
References
Index
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