Deep learning for brain disorders: from data processing to disease treatment.

Area of Science:

• Medical Informatics
• Computational Neuroscience
• Artificial Intelligence in Medicine

Background:

• Precision medicine and improved patient outcomes necessitate advanced analytical tools.
• Brain disorders present complex, heterogeneous challenges requiring multi-modal data analysis.
• Machine learning, particularly deep learning, offers powerful algorithms for complex data integration.

Purpose of the Study:

• To review the current applications of deep learning in understanding and treating brain disorders.
• To identify key deep learning architectures, data modalities, and specific brain disorders being studied.
• To propose guidelines for translating deep learning research into clinical settings.

Main Methods:

• Systematic literature review of deep learning applications in brain disorder research.
• Categorization of studies based on deep learning architecture, data types (demographic, clinical, imaging, genetics, environmental), and brain disorders.
• Analysis of trends and challenges in the field.

Main Results:

• Deep learning is increasingly applied across various brain disorders, leveraging multi-modal data.
• Identified common deep learning architectures (e.g., Convolutional Neural Networks, Recurrent Neural Networks) and data integration strategies.
• Highlighted the potential of deep learning to improve diagnostic accuracy and treatment personalization.

Conclusions:

• Deep learning shows significant promise for advancing brain disorder research and precision medicine.
• Bridging the gap between research findings and clinical implementation requires standardized methodologies and validation.
• Future directions include developing robust, interpretable deep learning models for routine clinical use.