Research Topic

Artificial Intelligence based Computer-aided Diagnosis Applications for Brain Disorders from Medical Imaging Data

About this Research Topic

There has been an exponential growth recently in research projects studying the etiology and mechanisms of several brain disorders, such as autism, Multiple sclerosis (MS), Alzheimer’s Disease (AD), Epilepsy and Parkinson’s. In recent years, the utility of artificial intelligence (AI) has been explored in a variety of research arenas for modern computer-aided diagnosis (CAD). The use of medical imaging and the characteristic examples provided by medical experts in AI-based CADs is a growing field for more accurate extraction of reliable diagnostic cues, which can eventually help physicians for more appropriate and personalized treatments. For instance, texture analyses of the white matter on brain T2-weighted magnetic resonance imaging (MRI) can help in diagnosing multiple sclerosis (MS).

Traditional machine learning-based CAD systems employ many learning techniques that are often tailored to a specific application and usually fail if tested outside the training data sets. Advances in AI techniques, particularly, end-to-end deep learning, combined with recent progress in neuroimaging technologies ( e.g., diffusion-weighted MRI and other modalities for visualizing the brain and the nervous system) have created exciting new opportunities for both enhancing traditional machine learning methods and applying prospective new ones to predict or provide better diagnosis of a patient’s brain.

The focus of this research topic is on the recent AI-based CAD systems for the analysis of medical imaging data from patients with brain disorders such as: MS, Autism, Alzheimer's, etc. Researchers around the globe are invited to contribute their original research articles and / or reviews stimulating technological advances in current applications in an effort to provide solutions to patients suffering from those devastating disorders.

We especially welcome submissions involving:

• The use of state-of-the-art deep learning and AI techniques to develop CAD systems for the diagnosis of multiple sclerosis from MRI data (e.g., T2- weighted)
• The use of state-of-the-art deep learning and AI techniques to develop CAD systems for the diagnosis of autism spectrum disorder from MR brain images (e.g., structural, functional, and diffusion tensor MRI).
• The use of state-of-the-art deep learning and AI techniques to develop CAD systems for the diagnosis of Alzheimer ’s disease from MRI data (such as structural, functional, and diffusion tensor MR images).
• The use of state-of-the-art deep learning and AI techniques to develop CAD systems for the diagnosis of other brain disorders, such as epilepsy and Parkinson’s disease using structural and functional MRI as well as SPECT.


Keywords: Machine Learning, AI, MRI, Deep Learning, Autism, Alzheimer's, Brain, Parkinson's


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.

There has been an exponential growth recently in research projects studying the etiology and mechanisms of several brain disorders, such as autism, Multiple sclerosis (MS), Alzheimer’s Disease (AD), Epilepsy and Parkinson’s. In recent years, the utility of artificial intelligence (AI) has been explored in a variety of research arenas for modern computer-aided diagnosis (CAD). The use of medical imaging and the characteristic examples provided by medical experts in AI-based CADs is a growing field for more accurate extraction of reliable diagnostic cues, which can eventually help physicians for more appropriate and personalized treatments. For instance, texture analyses of the white matter on brain T2-weighted magnetic resonance imaging (MRI) can help in diagnosing multiple sclerosis (MS).

Traditional machine learning-based CAD systems employ many learning techniques that are often tailored to a specific application and usually fail if tested outside the training data sets. Advances in AI techniques, particularly, end-to-end deep learning, combined with recent progress in neuroimaging technologies ( e.g., diffusion-weighted MRI and other modalities for visualizing the brain and the nervous system) have created exciting new opportunities for both enhancing traditional machine learning methods and applying prospective new ones to predict or provide better diagnosis of a patient’s brain.

The focus of this research topic is on the recent AI-based CAD systems for the analysis of medical imaging data from patients with brain disorders such as: MS, Autism, Alzheimer's, etc. Researchers around the globe are invited to contribute their original research articles and / or reviews stimulating technological advances in current applications in an effort to provide solutions to patients suffering from those devastating disorders.

We especially welcome submissions involving:

• The use of state-of-the-art deep learning and AI techniques to develop CAD systems for the diagnosis of multiple sclerosis from MRI data (e.g., T2- weighted)
• The use of state-of-the-art deep learning and AI techniques to develop CAD systems for the diagnosis of autism spectrum disorder from MR brain images (e.g., structural, functional, and diffusion tensor MRI).
• The use of state-of-the-art deep learning and AI techniques to develop CAD systems for the diagnosis of Alzheimer ’s disease from MRI data (such as structural, functional, and diffusion tensor MR images).
• The use of state-of-the-art deep learning and AI techniques to develop CAD systems for the diagnosis of other brain disorders, such as epilepsy and Parkinson’s disease using structural and functional MRI as well as SPECT.


Keywords: Machine Learning, AI, MRI, Deep Learning, Autism, Alzheimer's, Brain, Parkinson's


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.

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Submission Deadlines

24 March 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

24 March 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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