About this Research Topic
In last decade, artificial intelligence methods have been widely used in neurodegeneration diseases. Artificial intelligence methods, particularly machine-learning algorithms, would allow radiologists, scientists, and patients to address for early diagnostic tools, predict the longitudinal brain changes, and effective treatments for patients who suffer different types of neurodegenerative disorders.
The aim of this Research Topic is to present more robust artificial intelligence methods for applications in neurodegeneration disorders such as Alzheimer’s disease, Parkinson’s disease, motor neuron diseases, huntington’s disease, amyotrophic lateral sclerosis, frontotemporal lobar degeneration, and vascular cognitive impairment. We are interested to understand on how artificial intelligence models, particularly machine-learning algorithms, are being used, tailored, and specifically developed for neurodegeneration disorders. We are also keen to know how artificial intelligence methods can be used to address the clinical questions in the context of neurodegeneration, and, what neurologists can learn from artificial intelligence models in clinical settings.
This Research Topic welcomes original research papers or high-quality manuscripts covering state-of-the-art and novel algorithms, methodologies, and applications of artificial intelligence methods in neurodegenerative disorders. Topics of interest include but are not limited to:
• Artificial intelligence methods for early detection of neurodegeneration diseases
• Artificial intelligence in interventions and surgery
• Deep learning in neurodegeneration
• Computational intelligence methods in neurodegeneration
• Neuroimaging analysis
• Data fusion techniques (i.e., multimodal fusion) in neurodegeneration
• Fuzzy logic systems in neurodegeneration
Keywords: Algorithms, Classification, Prediction, Neurodegeneration, Brain Diseases, Neuroimaging, Deep Learning, Computational Intelligence, Data Fusion
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.