Abstract
INTRODUCTION
Efficient cerebral microhemorrhage (MCH) monitoring is critical for anti-amyloid therapy safety due to amyloid-related imaging abnormalities with hemosiderin deposition (ARIA-H) risk. We developed MCH-Guard, a multimodal machine-learning framework, to stratify MCH risk for Alzheimer’s Disease Neuroimaging Initiaitive (ADNI) participants (N = 813).
METHODS
Nested models integrated clinical history, fluid biomarkers, and imaging to predict MCH presence, incidence, and stability.
RESULTS
The comprehensive model detected baseline MCH with high accuracy (area under the curve [AUC] = 0.86). Notably, the minimal model (M1), utilizing only demographics and clinical history, achieved robust performance (AUC = 0.72). Longitudinal models predicted time-to-incidence (R
2 = 0.67) and stratified four-year risk. Furthermore, we identified a transient vascular instability phenotype–where MCH status fluctuates–which was strongly predicted by hepatic factors.
DISCUSSION
MCH-Guard offers a flexible clinical decision-support tool for optimizing spontaneous MCH and ARIA-H surveillance. The strong performance of the clinical-only model supports equitable risk assessment in resource-limited settings, while the characterization of vascular instability addresses a critical confounder in safety monitoring.
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This post is Copyright: | September 9, 2026
Neuro-Dementia