Brain Disease Diagnosis and Image Recognition

Dear Colleagues,

The intersection of artificial intelligence (AI) and medical imaging has revolutionized the diagnosis and understanding of brain diseases. With advancements in deep learning, neural computing, and multimodal data fusion, AI-driven tools now offer unprecedented precision in detecting neurological disorders such as Alzheimer’s disease, Parkinson’s disease, brain tumors, and stroke. These innovations address critical challenges in medical imaging, including noise reduction in low-quality scans, automated lesion segmentation, and early disease prediction. However, the complexity of brain systems spanning structural, functional, and molecular imaging modalities demands novel computational frameworks to integrate heterogeneous data and deliver clinically actionable insights.

This Special Issue aims to showcase cutting-edge research at the nexus of AI, neuroimaging, and clinical neuroscience. We invite contributions that leverage advanced algorithms (e.g., convolutional neural networks, transformers, and graph-based models) to enhance image reconstruction, feature extraction, and diagnostic accuracy in brain imaging. Emphasis will be placed on methodologies that bridge the gap between technical innovation and clinical utility, such as explainable AI for transparent decision-making, federated learning for privacy-preserving multi-center studies, and real-time processing for intraoperative guidance.

We welcome original research articles, reviews, and case studies that advance the theoretical foundations or practical applications of AI in brain disease diagnosis. Submissions should highlight scalability, generalizability, and validation across diverse populations to ensure translational impact. By fostering interdisciplinary collaboration among computer scientists, radiologists, and neuroscientists, this Special Issue seeks to accelerate the development of robust, equitable tools that redefine precision neurology.

Topics of interest include, but are not limited to the following:

• AI-driven MRI/CT/PET reconstruction and artifact correction;
• Automated lesion detection and segmentation in brain scans;
• Multimodal fusion of structural, functional, and molecular imaging data;
• Early prediction of neurodegenerative diseases using longitudinal imaging;
• Generative models for synthetic data augmentation;
• Edge computing and lightweight models for point-of-care diagnostics;
• Ethical AI frameworks for clinical deployment;
• Benchmarking datasets and reproducibility in neuroimaging AI.

Dr. Shumao Xu
Dr. He Wang
Dr. Minyan Ge
Guest Editors

Manuscript Submission Information

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• artificial intelligence
• brain disease diagnosis
• medical image recognition
• neuroimaging
• computational neuroscience
• deep learning
• machine learning
• computer vision
• signal processing
• biomedical image analysis
• neurodegenerative disorders
• brain–computer interface
• sleep disorders
• sleep–wake regulation
• polysomnography (PSG) analysis
• circadian rhythms
• sleep staging automation
• REM sleep detection
• insomnia biomarker identification
• sleep apnea detection
• neuroinflammation imaging
• multimodal data fusion
• explainable AI (XAI)
• federated learning
• generative adversarial networks (GANs)
• edge computing for healthcare
• clinical decision support systems
• precision neurology
• biomarker discovery
• sleep-related neurodegeneration
• EEG signal processing
• functional MRI (fMRI)
• diffusion tensor imaging (DTI)
• Alzheimer’s disease prediction
• Parkinson’s disease biomarkers
• brain tumor segmentation
• stroke lesion mapping
• ethical AI in healthcare
• AI-driven sleep medicine
• automated sleep scoring