Enhancing Early Diagnosis: Multimodal AI Approaches for Neurodegenerative Diseases

Area of Science:

• Neurology
• Biomedical Engineering
• Data Science

Background:

• Neurodegenerative diseases like Alzheimer's and Parkinson's present a significant global health challenge.
• Current diagnostic methods often detect disease only after substantial irreversible neuronal damage has occurred.
• A critical gap exists between early pathological changes and clinical manifestation, hindering timely intervention.

Purpose of the Study:

• To review the application of artificial intelligence (AI)-driven biomarkers in neurodegenerative disease detection.
• To evaluate how machine learning models extract predictive patterns from diverse data sources.
• To highlight the potential of AI to bridge the diagnostic gap and enable proactive healthcare.

Main Methods:

• Analysis of machine learning models applied to neuroimaging (MRI, PET) and electrophysiology (EEG) data.
• Evaluation of digital phenotyping for early deviation detection.
• Synthesis of evidence on multimodal data fusion architectures.
• Assessment of challenges in clinical translation, including data heterogeneity and explainability.

Main Results:

• AI-driven biomarkers can identify preclinical neurodegenerative changes earlier than traditional markers.
• Machine learning models extract high-dimensional, subvisual patterns indicative of early pathology.
• Multimodal AI approaches demonstrate superior performance in capturing complex neurodegenerative processes.
• AI facilitates a shift towards continuous, temporally informed disease modeling.

Conclusions:

• AI biomarkers offer a transformative paradigm for proactive neurodegenerative disease management.
• Integrating explainable AI with longitudinal data can redefine diagnosis towards precision risk stratification and early intervention.
• Addressing challenges like data equity and model transparency is crucial for clinical translation.