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Machine Learning (ML) and Deep Learning (DL) have become essential
tools in healthcare. They are capable of processing enormous
amounts of data to find patterns and are also adopted into methods
that manage and make sense of healthcare data, either electronic
healthcare records or medical imagery. This book explores how ML/DL
can assist neurologists in identifying, classifying or predicting
neurological problems that require neuroimaging. With the ability
to model high dimensional datasets, supervised learning algorithms
can help in relating brain images to behavioral or clinical
observations and unsupervised learning can uncover hidden
structures/patterns in images. Bringing together both AI experts as
well as medical practitioners, chapters cover the majority of neuro
problems that use neuroimaging for diagnosis, along with case
studies and directions for future research.
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.
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