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Elsevier

Artificial Intelligence in Brain Disorders

Innovations in Diagnosis and Treatment

  • 1 Edición - 1 de octubre de 2026
  • Última edición
  • Editores: Pranav Kumar Prabhakar, Arun Kumar Singh, Prateek Agrawal, Radu Prodan
  • Idioma: Inglés

Artificial Intelligence in Brain Disorders: Innovations in Diagnosis and Treatment focuses on the utilization of AI and machine learning to enhance current practices in the diagno… Leer más

Descripción

Artificial Intelligence in Brain Disorders: Innovations in Diagnosis and Treatment focuses on the utilization of AI and machine learning to enhance current practices in the diagnosis and treatment of neurological disorders. Each chapter provides in-depth exploration of specific areas where AI can improve existing methodologies, offering practical guidance, case studies, and research findings that can be directly applied in the field. It explains the application of AI in diagnosing and treating major neurological illnesses and showcases the potential of AI in predicting diseases such as epilepsy and neurodegenerative disorders.

As such, this book offers a detailed overview of AI and machine learning techniques relevant to neurological research.

Puntos claves

  • Offers a comprehensive exploration of cutting-edge AI, big data analytics, and machine learning methodologies specifically applied in the field of neurology
  • Presents various machine learning techniques such as image segmentation, classification, neural networks, and image processing
  • Showcases techniques in diagnosing early neurological disease identification and deep learning applications using advanced brain imaging technologies like EEG, MEG, fMRI, fNIRS, and PET
  • Provides practical insights and case studies

De interès para

Researchers, faculty, and industry practitioners within the fields of Computer Science, Biomedical Engineering, and Software Engineering, with specific relevance to Bioinformatics

Índice

1. An Overview of Artificial Intelligence-Based Imaging Techniques

2. Identification and evaluation of low-grade gliomas in the brain using machine learning

3. Cognitive therapy for brain diseases using deep learning models

4. Machine Intelligence in Clinical Neuroscience

5. Brain Informatics, by NL Swathy, Department Of Pharmacy Practice

6. Alzheimer's Disease Diagnosis using Artificial Intelligence

7. AI-enabled assistance support to Alzheimer Patients

8. Adolescents with serious depressive disorder: AI for detecting mental disorders

9. Modelling cognitive impairment in Parkinson s patients using deep learning

10. Parkinson’s disease diagnosis using AI

11. Artificial Intelligence shaping the future of neurology practice

12. Convergence of Artificial Intelligence and Neuroscience for Neurological Disorder Diagnosis

13. Early detection and prediction of brain tumurs in human patients using deep learning

14. Using deep learning to identify Brain tumors

15. Combining artificial intelligence and neuroscience to diagnose and predict neurological diseases

16. The role of AI in neuroethics and patients’ privacy

Detalles del producto

  • Edición: 1
  • Última edición
  • Publicado: 1 de octubre de 2026
  • Idioma: Inglés

Sobre los editores

PP

Pranav Kumar Prabhakar

Dr. Pranav Kumar Prabhakar is currently working as a Professor and Head at Department of Biotechnology, School of Engineering and Technology, Nagaland University, Meriema, Kohima, Nagaland, India. He is among the World’s Top 2% Scientists (list published by Stanford University, USA, 2021, 2022, 2023, 2024, and 2025). He completed his PhD in Biotechnology from IIT Madras. His research focuses on elucidating molecular mechanisms and strategies for oral insulin delivery and mimicking signaling pathways in metabolic disorders (diabetes) using natural products. Dr. Pranav is a member of the Royal Society of Chemistry and the Asia-Pacific Chemical, Biological & Environmental Engineering Society. He serves as an editorial board member and reviewer for many reputed national and international journals. His honors include a travel grant from IIT Madras and the Council for Scientific and Industrial Research (CSIR) to attend ATTD 2009 in Greece, approved by the Department of Science and Technology (DST). He has published over 160+ research articles, authored/edited 24 books, and 48 book chapters, and delivered 9 oral and poster presentations at scientific meetings.

Afiliaciones y experiencia
Professor and Head, Department of Biotechnology, School of Engineering and Technology, Nagaland University, Meriema, Kohima, Nagaland, India

AS

Arun Kumar Singh

Arun Kumar Singh is an Assistant Professor in the Department of Pharmacy at University School of Pharmaceutical Sciences, Rayat Bahra University V.P.O. Sahauran , India. He completed his M.Pharm in Pharmaceutics from Galgotias University, Greater Noida. His research interests include nano-formulation, blockchain, IoT, machine learning, artificial intelligence, big data analytics, cancer biology, and neuroscience. He has contributed to academic books and book chapters and has published review articles in the areas of pharmaceutical sciences and healthcare technology. His work focuses on interdisciplinary approaches integrating pharmaceutical research with emerging digital and computational technologies.
Afiliaciones y experiencia
Assistant Professor, University School of Pharmaceutical Sciences, Rayat Bahra University V.P.O. Sahauran, Tehsil Kharar, Distt. Mohali, Punjab-140104, INDIA

PA

Prateek Agrawal

Prateek Agrawal is professor and deputy dean at the School of Computer Science & Engineering, Lovely Professional University, Phagwara, Punjab, India. His research areas include natural language processing, computer vision, video processing, expert systems, deep learning applications, and other related topics. He is a senior member of IEEE and core member of IEEE India Council for Sustainable Development Activity, and is also a member of different reputed organizations like IET, MIR lab, and IAENG among others. Dr. Agarwal has published over 70 research papers in Scopus/SCIE indexed journals and conferences, 60 national patents, five edited books, and 10 book chapters. He is book series editor of the IOP series on next generation computing, and is a reviewer for many SCIE journals like Multimedia tools and Applications, Plos One, PeerJ, Oxford computer science, IEEE Access, and Ambient Intelligent & Humanized Computing.

Afiliaciones y experiencia
Professor and Deputy Dean, School of Computer Science & Engineering, Lovely Professional University, Phagwara, Punjab, India

RP

Radu Prodan

Radu Prodan is professor of distributed systems at the Institute of Information Technology (ITEC), University of Klagenfurt, Austria. He was an associate professor at the University of Innsbruck until 2018. His research interests include performance, optimization, and resource management tools for parallel and distributed systems, as well as middleware system tools for cloud, fog, and edge computing. He has participated in numerous projects, including coordinating the Horizon 2020 project ARTICONF. He has coauthored over 200 publications and received three IEEE best paper awards. He is a member of ACM.

Afiliaciones y experiencia
Professor, Distributed Systems, Institute of Information Technology (ITEC), University of Klagenfurt, Austria