Offcanvas Shape

Narayan Vyas, an academician at Vivekananda Global University, specializes in computer science, focusing on IoT and Mobile App Development. He has trained over 1000 students globally and published extensively in Scopus journals. An active IEEE member, his research interests include Remote Sensing, Machine Learning, and Computer Vision.

Get In Touch

AI-Driven Alzheimer's Disease Detection and Prediction

AI-Driven Alzheimer's Disease Detection and Prediction

Price: $425.00 | Publisher: IGI Global

Editors: Umesh Kumar Lilhore, Abhineet Anand, Abhishek Kumar, Satya Prakash Yadav, Narayan Vyas

Projected Release Date: June 2024 | Copyright: © 2024 | Language: English | Categories: Healthcare, Technology, AI

ISBN: 978-8-3693-3605-2Coming Soon

Alzheimer's disease (AD) poses a significant global health challenge, with an estimated 50 million people affected worldwide and no known cure. Traditional methods of diagnosis and prediction often rely on subjective assessments. They are limited in detecting the disease early, leading to delayed intervention and poorer patient outcomes. Additionally, the complexity of AD, with its multifactorial etiology and diverse clinical manifestations, requires a multidisciplinary approach for effective management.

AI-Driven Alzheimer's Disease Detection and Prediction offers a groundbreaking solution by leveraging advanced artificial intelligence (AI) techniques to enhance early diagnosis and prediction of AD. This edited book provides a comprehensive overview of state-of-the-art research, methodologies, and applications at the intersection of AI and AD detection. By bridging the gap between traditional diagnostic methods and cutting-edge technology, this book facilitates knowledge exchange, fosters interdisciplinary collaboration, and contributes to innovative solutions for AD management.

It also benefits data scientists, engineers, policymakers, and professionals in the pharmaceutical and biotechnology industries. Graduate students interested in healthcare and technology will find accessible information on the latest developments in AI-driven approaches to AD detection and prediction.

Preorder Now
  • Chapter 1: Introduction to Alzheimer’s Disease, Biomarkers, and the AI Revolution

    Authors: Bancha Yingngam

  • Chapter 2: Neuroimaging and Biomarkers in AD Detection

    Authors: Komal Thapa, Neha Kanojia, Ameya Sharma, Lata Rani Rani, Vivek Puri

  • Chapter 3: Integrating AI in Alzheimer's Disease Management - A Strategic Approach for Healthcare Administrators

    Authors: Mazharunnisa Md, Prem Chowary V, Sai B.V., Anuradha T., Indhu Sri

  • Chapter 4: Advanced Deep Learning Approaches for Alzheimer's Disease: Enhancing Diagnostic Classification and Prognostic Prediction

    Authors: RAMAKRISHNA KUMAR, Yogesh Kumar Sharma, Monika Dhananjay Rokade, Hidayath Ali Baig

  • Chapter 5: Advancements in Alzheimer's Diagnosis - A Comprehensive Exploration of AI-Powered Diagnostic Tools and Software

    Authors: Vatsala Tomar, Arvind Kumar, Vandana Kate, Sandeep Kumar, Prasanthi Gottumukkala

  • Chapter 6: AI-Powered Paradigm Shift: Non-Invasive Biomarkers for Early Detection of Alzheimer’s Disease

    Authors: Yogesh Kumar Sharma, RAMAKRISHNA KUMAR, Hidayath Baig, Sunil Sudam Khatal

  • Chapter 7: AI-Enhanced Drug Discovery for Alzheimer's

    Authors: D.R. Denslin Brabin, Muralidharan J, Sharath Kumar Jagannathan, Ruth Ramya Kalangi

  • Chapter 8: AI in Neurodegeneration Prediction - Exploring Advanced Approaches for Alzheimer's Disease Progression

    Authors: Neelima Priyanka Nutulapati, Naresh Babu Karunakaran, Banupriya V, Malatthi Sivasundaram, Venkata Ramana Kaveripakam

  • Chapter 9: Strategic Management of AI-Enhanced Alzheimer's Disease Prediction Models - Navigating Ethical and Regulatory Frontiers

    Authors: Brindha Devi, Thaiyalnayaki M., Vasantha S

  • Chapter 10: Unravelling AI and Machine Learning Essentials in Alzheimer's Research

    Authors: Saravanan V, Ruth Ramya Kalangi, Saravanan Thangavel, Venkata Ramana K

  • Chapter 11: Revolutionizing Alzheimer's Diagnosis- Navigating the Challenges and Embracing Opportunities in the Clinical Integration of AI-Powered Tools

    Authors: Ananda Kumar K S, Senthil Kumar A, Gireesh H R, Shashidhar V

  • Chapter 12: Unravelling Alzheimer's-The AI Revolution in Diagnosis and Prediction

    Authors: Suja G P

  • Chapter 13: Role of Artificial Intelligence in Cognitive Assessment & Early Detection of Alzheimer’s Disease: Cognitive Assessment & AI

    Authors: Deepak Panwar, Parul Sharma, Shweta Sharma, Manu Goyal, Kanu Goyal

  • Chapter 14: Unravelling Data Challenges in AI-Driven Alzheimer's Research

    Authors: Arunadevi B, Vidyabharathi Dakshinamurthi, Bennilo Fernandes J, Sharmiladevi D.

  • Chapter 15: Revolutionizing Alzheimer's Diagnosis- Navigating the Challenges and Embracing Opportunities in the Clinical Integration of AI-Powered Tools

    Authors: Ananda Kumar K S

  • Chapter 16: Unveiling Alzheimer's Early Signs - AI-driven Insights through Neuroimaging and Biomarkers

    Authors: Ramani S, Madiajagan M, Shikha Maheshwari, Utpal Saikia

  • Chapter 17: Real-World Impact - Case Studies and Success Stories in AI-driven Alzheimer's Disease Research and Care

    Authors: Kireet Muppavaram, Amit Gangopadhyay, Sudhir Ramadass, Prakash N, Siva Shankar S

  • Chapter 18: Patient-Centered AI Solutions for Managing Alzheimer's Disease

    Authors: Sheerin Banu M, D Sugumar, Sujatha S, Veeramakali T

  • Chapter 19: Navigating the Administrative Landscape of AI in Alzheimer's Disease Detection - A Comprehensive Management Studies Perspective

    Authors: Mohana Krishna I, Lingamsetty Dhanush, Chinni Tejasri, A Hari Teja, K S N L Trisha, NEHA IRRINKI

  • Chapter 20: Machine Learning Models for Alzheimer's disease Detection - An In-Depth Exploration, Including Deep Learning Approaches

    Authors: Indira Bharathi, Swarnasudha M, Manjula S, Gethzi Ahila Poornima I

  • Chapter 21: Intelligent Techniques for Detection and Diagnosis of Neurodegenerative Diseases

    Authors: DEEPAK VARADAM, Sahana Shankar, Pranathi Hegde, Shobitha V, Sunidhi M, Sanjana N

  • Chapter 22: Integrating Genomic Data and Genetic Risk Factors with AI for Predicting Susceptibility to Alzheimer's Disease

    Authors: Nikhat Parveen, Vidyabharathi D, Nazeer Shaik, Ram Nivas D

  • Chapter 23: Global Initiatives and Collaborations in AI for Alzheimer's Disease

    Authors: A Chandrashekhar, Nikhat Parveen, Muthumari A., Menaga D.

  • Chapter 24: Challenges and Future Directions in AI-driven Alzheimer's Disease Research and Care

    Authors: Balachandra Pattanaik, Rambabu Nalagandla, Varagantham Anitha Avula, Kumarasamy M., Ojasvi Pattanaik

  • Chapter 25: Educating Health Care Professional on AI in Alzheimer Disease Strategies and Programmes for Educating Health Care Professional on the Use of AI in Alzheimer's

    Authors: Shweta Sharma, Shavez Mansoori, Manu Goyal, Kanu Goyal, Parul Sharma

  • Chapter 26: Cognitive Assessment and Early Detection of Alzheimer's disease - Harnessing AI through Tasks and Games

    Authors: Deena G, Naresh Babu Karunakaran, Saumya Sharma, Vivekanand Pandey, Neerav Nishant

  • Chapter 27: Ethical and Privacy Considerations in AI-Driven AD Research

    Authors: Mohammed Abdul Matheen, Zainulabedin Hasan, Amairullah Khan Lodhi, Shaikh Abdul Waheed, Altaf C

  • Chapter 28: Exploring The Role of Natural Learning Processing in Alzheimer’s Disease Research and Prediction: NLP in AD Research

    Authors: Ruchi Jakhmola Mani, Yusra Ashfaque Ali, Deepshikha Pande Katare, Snigdha Debashis Bhattacharjee, Prathum Pathak

Showing all Related Products:

Applying Machine Learning Techniques to Bioinformatics
Innovations in Machine Learning and IoT for Water Management
Quantum Machine Learning: Quantum Algorithms and Neural Networks

Need Help?

Book an Appointment for Expert Consultancy Schedule a session for Mobile App Development consultancy or Research Guidance tailored to your needs.