Advancements in Imaging Technologies and AI Integration for Neurodegenerative Disease Management: A Narrative Review

Neurodegenerative diseases, characterized by progressive neuronal degeneration, are increasingly prevalent due to global aging trends and impose a significant burden on patients. No cure currently exists, with oxidative stress and inflammation serving as key drivers of disease progression. Advances in imaging technologies and artificial intelligence (AI) offer new opportunities for early diagnosis, monitoring, and treatment evaluation. This review aims to summarize the role of advanced neuroimaging modalities and AI integration in improving the diagnosis, monitoring, and management of neurodegenerative diseases, while highlighting current challenges and future directions.

A narrative review was conducted based on published literature on neuroimaging techniques in neurodegenerative diseases. Key modalities included structural and functional magnetic resonance imaging (MRI, fMRI), diffusion tensor imaging (DTI), positron emission tomography (PET), and single-photon emission computed tomography (SPECT). The integration of AI in image analysis was evaluated for its impact on diagnostic accuracy and workflow efficiency. Sources were selected from peer-reviewed journals focusing on clinical applications, technical advancements, and multimodal imaging strategies. Results Structural MRI, fMRI, and DTI provide detailed insights into brain atrophy and microstructural integrity, while PET and SPECT enable molecular-level assessment of metabolism and pathology. AI-enhanced analysis reduces interpretation variability and improves diagnostic precision. Despite these advances, high costs, limited accessibility, and inter-expert subjectivity remain major barriers. Emerging multimodal approaches and AI-driven tools show promise in enabling earlier detection and personalized treatment monitoring.

The integration of advanced imaging and AI holds transformative potential for neurodegenerative disease management. Future efforts should prioritize cost reduction, improved accessibility, and seamless multimodal data fusion to translate these technologies into routine clinical practice.