Implementing a tridimensional diagnostic framework for personalized medicine in neurodegenerative diseases

Area of Science:

• Neuroscience
• Medical Diagnostics
• Genetics

Background:

• Neurodegenerative diseases (NDDs) present diagnostic challenges due to clinical heterogeneity and multifactorial causes.
• Traditional diagnostic methods struggle to integrate complex genetic, molecular, and neuroanatomical data.
• Existing nosologies are limited in capturing the full spectrum of NDDs and guiding personalized interventions.

Purpose of the Study:

• To introduce a novel tridimensional diagnostic framework for neurodegenerative diseases.
• To enhance diagnostic accuracy and clinical relevance by integrating diverse etiological, molecular, and neuroanatomical data.
• To facilitate precision medicine approaches in managing NDDs.

Main Methods:

• Developed a tridimensional framework with axes for etiology, molecular markers, and neuroanatomoclinical correlations.
• Integrated omics technologies, neuroimaging, and AI-driven probabilistic modeling.
• Utilized case studies to demonstrate framework application in patient stratification and treatment guidance.

Main Results:

• The framework synthesizes incomplete datasets for improved diagnostic accuracy.
• Demonstrated ability to stratify patients based on integrated etiological, molecular, and clinical profiles.
• Case studies showed practical implementation for guiding personalized treatment strategies.

Conclusions:

• The tridimensional framework offers a scalable and adaptable tool for NDD diagnosis and research.
• This approach addresses the limitations of traditional nosologies and enhances precision medicine in NDD management.
• The system supports the integration of disease-modifying therapies (DMTs) with symptomatic treatments for improved patient outcomes.