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.
1. Cover image - p. Cover 2. Title page - p. iii 3. Table of Contents - p. iii 4. Copyright - p. iv 5. List of contributors - p. xiii 6. 1. Intracranial hemorrhage detection and classification using deep learning - p. 1 7. 2. Deep learning for noninvasive management of brain tumors - p. 15 8. 3. Artificial intelligence in Parkinson’s disease—symptoms identification and monitoring - p. 35 9. 4. Alzheimer’s disease detection using artificial intelligence - p. 53 10. 5. Intelligent computer systems for multiple sclerosis diagnosis - p. 75 11. 6. Current and future applications of artificial intelligence in multiple sclerosis - p. 107 12. 7. Artificial intelligence–assisted headache classification: a review - p. 145 13. 8. Deep learning for reliable detection of epileptogenic lesions - p. 163 14. 9. Artificial intelligence in neurosciences—are we really there? - p. 177 15. 10. Artificial intelligence in the management of neurological disorders: its prevalence and prominence - p. 193 16. 11. Graphical assessment of the internal structure of Parkinsons dataset—a case study - p. 223 17. 12. Applications of artificial intelligence to neurological disorders: current technologies and open problems - p. 243 18. 13. Developing a chatbot/intelligent system for neurological diagnosis and management - p. 273 19. 14. Artificial intelligence in the diagnosis and management of acute ischemic stroke - p. 293 20. 15. Towards intelligent extended reality in stroke rehabilitation: Application of machine learning and artificial intelligence in rehabilitation - p. 309 21. Index - p. 331
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.
1. Cover image - p. Cover 2. Title page - p. iii 3. Table of Contents - p. iii 4. Copyright - p. iv 5. List of contributors - p. xiii 6. 1. Intracranial hemorrhage detection and classification using deep learning - p. 1 7. 2. Deep learning for noninvasive management of brain tumors - p. 15 8. 3. Artificial intelligence in Parkinson’s disease—symptoms identification and monitoring - p. 35 9. 4. Alzheimer’s disease detection using artificial intelligence - p. 53 10. 5. Intelligent computer systems for multiple sclerosis diagnosis - p. 75 11. 6. Current and future applications of artificial intelligence in multiple sclerosis - p. 107 12. 7. Artificial intelligence–assisted headache classification: a review - p. 145 13. 8. Deep learning for reliable detection of epileptogenic lesions - p. 163 14. 9. Artificial intelligence in neurosciences—are we really there? - p. 177 15. 10. Artificial intelligence in the management of neurological disorders: its prevalence and prominence - p. 193 16. 11. Graphical assessment of the internal structure of Parkinsons dataset—a case study - p. 223 17. 12. Applications of artificial intelligence to neurological disorders: current technologies and open problems - p. 243 18. 13. Developing a chatbot/intelligent system for neurological diagnosis and management - p. 273 19. 14. Artificial intelligence in the diagnosis and management of acute ischemic stroke - p. 293 20. 15. Towards intelligent extended reality in stroke rehabilitation: Application of machine learning and artificial intelligence in rehabilitation - p. 309 21. Index - p. 331
