Research Topic

Artificial Intelligence Based Diagnostics for Neurological Disorders

About this Research Topic

Diagnostic imaging has experienced major changes in recent years. Radiological and nuclear medicine modalities represent the option of choice to investigate the main neurological diseases. The improving capabilities of the imaging devices and the increasing availability of storing, sharing and computing facilities have been generating larger and larger amounts of data. Consequently, there has been increasing attention on the development of computational methods for the extraction of objective imaging features (biomarkers) capable of correlating with disease phenotype, clinical outcome and/or response to treatment. The combined use of imaging data, biomarkers and artificial intelligence techniques makes it possible to build powerful predictive models which can assist the physician in the management of patients with a wide range of neurological disorders, ultimately leading to personalized treatment and better clinical outcome. However, there are still open challenges before these methods can be translated into clinical practice. Critical to this process, for instance, are standardization, strong interdisciplinary cooperation, and the availability of centralized repositories of annotated data.

This Research Topic will provide a forum to discuss challenges, discoveries and opportunities in the field, with specific focus on using AI methods in the diagnosis of oncological and neurological disorders by radiological and nuclear medicine modalities. We encourage the submission research papers as well as review articles; comparative evaluations and new datasets are also welcome.

We welcome articles on the following topics:

· AI Epilepsy predictors

· Deep learning for autism

· EEG based Machine learning methods to detect Parkinson’s disease

· Multimodal Physiological signal based automatic detection of Alzheimer’s disease

· EEG based automated detection of various types of epilepsy

· Patient specific Automatic Grading of multiple sclerosis

· AI based diagnostics for brain tumor

· Automatic Localization of Glioma from MRI Images

· MRI Image based AI diagnostics for neural disorders


Keywords: deep learning, biomedical applications, imaging, artificial intelligence, brain tumour


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.

Diagnostic imaging has experienced major changes in recent years. Radiological and nuclear medicine modalities represent the option of choice to investigate the main neurological diseases. The improving capabilities of the imaging devices and the increasing availability of storing, sharing and computing facilities have been generating larger and larger amounts of data. Consequently, there has been increasing attention on the development of computational methods for the extraction of objective imaging features (biomarkers) capable of correlating with disease phenotype, clinical outcome and/or response to treatment. The combined use of imaging data, biomarkers and artificial intelligence techniques makes it possible to build powerful predictive models which can assist the physician in the management of patients with a wide range of neurological disorders, ultimately leading to personalized treatment and better clinical outcome. However, there are still open challenges before these methods can be translated into clinical practice. Critical to this process, for instance, are standardization, strong interdisciplinary cooperation, and the availability of centralized repositories of annotated data.

This Research Topic will provide a forum to discuss challenges, discoveries and opportunities in the field, with specific focus on using AI methods in the diagnosis of oncological and neurological disorders by radiological and nuclear medicine modalities. We encourage the submission research papers as well as review articles; comparative evaluations and new datasets are also welcome.

We welcome articles on the following topics:

· AI Epilepsy predictors

· Deep learning for autism

· EEG based Machine learning methods to detect Parkinson’s disease

· Multimodal Physiological signal based automatic detection of Alzheimer’s disease

· EEG based automated detection of various types of epilepsy

· Patient specific Automatic Grading of multiple sclerosis

· AI based diagnostics for brain tumor

· Automatic Localization of Glioma from MRI Images

· MRI Image based AI diagnostics for neural disorders


Keywords: deep learning, biomedical applications, imaging, artificial intelligence, brain tumour


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

09 August 2021 Abstract
07 December 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

09 August 2021 Abstract
07 December 2021 Manuscript

Participating Journals

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

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