Biomedicines, Vol. 13, Pages 2739: From Lesion to Decision: AI for ARIA Detection and Predictive Imaging in Alzheimer’s Disease

Biomedicines doi: 10.3390/biomedicines13112739

Authors:
Rafail C. Christodoulou
Platon S. Papageorgiou
Maria Daniela Sarquis
Ludwing Rivera
Celimar Morales Gonzalez
Daniel Eller
Gipsany Rivera
Vasileia Petrou
Georgios Vamvouras
Evros Vassiliou
Sokratis G. Papageorgiou
Michalis F. Georgiou

Background: Alzheimer’s disease (AD) remains the leading cause of dementia worldwide, with anti-amyloid monoclonal antibodies (MABs) marking a significant advance as the first disease-modifying therapies. Their use, however, is limited by amyloid-related imaging abnormalities (ARIA), which appear as vasogenic edema or effusion (ARIA-E) and hemosiderin-related changes (ARIA-H) on MRI. Variability in imaging protocols, subtle early findings, and the lack of standardized risk models challenge detection and management. Methods: This narrative review summarizes current artificial intelligence (AI) applications for ARIA detection and risk prediction. A comprehensive literature search across PubMed, Embase, and Scopus identified studies focusing on MRI-based AI analysis, lesion quantification, and predictive modeling. Results: The evidence is organized into six thematic domains: ARIA definitions, imaging challenges, foundations of AI in neuroimaging, detection tools, predictive frameworks, and future perspectives. Conclusions: AI offers promising avenues to standardize ARIA evaluation, improve lesion quantification, and enable individualized risk prediction. Progress will depend on multicenter datasets, shared frameworks, and prospective validation. Ultimately, AI-driven neuroimaging may transform how treatment-related complications are monitored in the era of anti-amyloid therapy.

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Rafail C. Christodoulou www.mdpi.com