AI-Enhanced Neuroimaging: Transforming Neurodegenerative Disease Management

Neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease present significant challenges due to their complex pathophysiology and varied clinical manifestations. These complexities often hinder accurate diagnosis and effective treatment. Neuroimaging techniques like MRI, PET, and CT have become indispensable in the early detection and management of these disorders. Recently, artificial intelligence (AI) and machine learning (ML) have revolutionized neuroimaging, allowing for the analysis of detailed imaging patterns that traditional methods cannot discern. Through these AI-driven advancements, there is a promising potential to deepen the understanding of disease processes, enhance diagnostic precision, and open avenues for personalized patient care.

The primary aim of this Research Topic is to explore and showcase the integration of AI technologies with neuroimaging techniques to improve the clinical management and scientific understanding of neurodegenerative diseases. We intend to spotlight pioneering research that utilizes AI and ML to discover hidden biomarkers, forecast disease progression, accurately differentiate between various syndromes, and find personalized treatments. By encouraging cross-disciplinary collaborations, this Research Topic seeks to expedite the application of AI discoveries to practical healthcare solutions, ultimately enhancing patient outcomes. We welcome contributions that highlight innovative models, cutting-edge imaging methodologies, and digital health advancements that collectively forecast a promising future for computational neuroimaging.

Articles sought for this Research Topic—including Original Research, Brief Research Reports, Perspectives, Protocols, Mini-Reviews, and more—should focus on transformative AI applications in neuroimaging within the domain of neurodegenerative disease management. Suggested themes include, but are not limited to:

- Exploration of AI for early prediction and detection of degenerative disorders.

- Analysis of brain connectivity and its changes through advanced imaging data.

- Development of predictive models for accurate diagnosis and monitoring.

- Differentiation techniques for overlapping neurodegenerative conditions.

- Biomarker discovery advancements using neuroimaging techniques.

- Integration of digital and wearable technologies alongside traditional neuroimaging practices.

Submissions should highlight the methodological clinical significance and translational impact, providing comprehensive insights into the potential of AI-driven neuroimaging to improve neurodegenerative disease management.

Articles accepted after peer review will be published and appear online as soon as approved for publication.

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Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Study Protocol
• Systematic Review

Keywords: artificial intelligence, neuroimaging, neurodegenerative diseases, MRI, PET, CT, machine learning

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.