GMN4AD: Graph Matching Network for Alzheimer's Disease Diagnosis with Test-Time Domain Adaptation using Multi-centered Structure Magnetic Resonance Imaging
arXiv:2606.13919v1 Announce Type: cross Abstract: Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that affects millions of older adults, with prevalence expected to rise significantly in the coming years. Early diagnosis, particularly during the mild cognitive impairment (MCI) stage, is critical for timely intervention. Structural Magnetic Resonance Imaging (sMRI) has emerged as a key modality for detecting AD-related brain changes, but traditional graph-based approaches often struggle with modality and inter-site heterogeneity, limiting diagnostic performance. In this pap
The increasing prevalence of Alzheimer's Disease and advancements in AI, particularly graph-based machine learning, are converging to demand and enable more sophisticated diagnostic tools.
Improved early diagnosis of Alzheimer's Disease using AI can significantly impact patient outcomes, healthcare costs, and research into neurodegenerative disorders, offering a path to more timely interventions.
The proposed 'Graph Matching Network' approach offers a more robust and adaptable method for AD diagnosis from sMRI, potentially overcoming current limitations in heterogeneity and diagnostic performance.
- · AI in healthcare
- · Medical imaging companies
- · Pharmaceuticals (early intervention)
- · Elderly care sector
- · Traditional diagnostic methods
- · Healthcare systems unprepared for data integration
More accurate and earlier diagnosis of Alzheimer's Disease.
Accelerated development of treatments for early-stage Alzheimer's, as larger and better-diagnosed patient cohorts become available for trials.
Potential for predictive AI models to identify individuals at high risk for AD before symptom onset, enabling preventative care and lifestyle interventions.
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Read at arXiv cs.AI