Academic Press Augmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligence

Product 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. Review Examines the application of artificial intelligence in the diagnosis and rehab of a wide variety of neurological disorders From the Back Cover Deciphering the massive volume of healthcare electronic data has the potential to reform modern medicine, as well as present significant challenges. Artificial Intelligence is capable of churning through enormous amounts of data to find patterns and is also adopted into methods that manage and make sense of brain activity. The field of Artificial Intelligence (AI) with its subfields such as Machine Learning (ML), Computer Vision, Natural Language Processing (NLP) and Deep learning (DL) are very well suited to address these challenges, and present effective systems for identifying as well as predicting various neurological disorders. Augmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligence focuses on how neurosciences can benefit from these 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. Stroke is a vascular disease of the brain where time and salvageable tissue are the key factors which determine the treatment. Even with the advances in the neuroimaging, there is time lost in reading the images. The chapters on stroke help readers learn how AI helps expedite stroke treatment. These 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. Alzheimer's Disease often goes undetected for long periods of time due to deficiencies in screening and detection accuracy. AI can help reduce this time lag significantly. Nearly 30 percent of Epilepsy in patients is refractory. The chapters on Epilepsy demonstrate the use of Machine Learning techniques to detect epileptogenic lesions on MRI, understanding the neural network generating the Epilepsy. Parkinson's Disease is a common neurodegenerative disease affecting about 1% of the world population predominantly over the age of 55. The chapter on Parkinson’s Disease demonstrates the utility of AI for assisting with diagnostics and assessment of Parkinson's disease. About the Author 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, A