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Related Experiment Video

Updated: Jul 4, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

MCH-Guard: Multimodal Machine Learning Framework for Risk Stratification of Cerebral Microhemorrhage Risk in the

Alper Gel, Eliana Phillips, Isabella Hausle

    Medrxiv : the Preprint Server for Health Sciences
    |July 3, 2026
    PubMed
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    MCH-Guard, a machine learning tool, accurately predicts cerebral microhemorrhage (MCH) risk for anti-amyloid therapy safety. A clinical-only model offers equitable risk assessment, even in limited-resource settings.

    Area of Science:

    • Neuroimaging
    • Biomarkers
    • Machine Learning in Medicine

    Background:

    • Cerebral microhemorrhage (MCH) monitoring is crucial for anti-amyloid therapy safety due to ARIA-H risk.
    • A multimodal machine learning framework, MCH-Guard, was developed to stratify MCH risk.
    • The study utilized data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort (N=813).

    Purpose of the Study:

    • To develop and validate MCH-Guard, a machine learning framework for MCH risk stratification.
    • To assess the predictive performance of models integrating clinical, fluid biomarker, and imaging data.
    • To evaluate the utility of a clinical history-only model for equitable risk assessment.

    Main Methods:

    • Developed nested machine learning models integrating clinical history, fluid biomarkers, and neuroimaging data.

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    Related Experiment Videos

    Last Updated: Jul 4, 2026

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
    12:18

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

    Published on: January 11, 2020

    Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
    08:43

    Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

    Published on: August 7, 2017

  • Predicted MCH presence, incidence, and stability using the developed models.
  • Evaluated model performance using Area Under the Curve (AUC) for cross-sectional prediction and R-squared for longitudinal prediction.

    • Main Results:

    • The comprehensive model achieved high accuracy (AUC 0.86) in detecting baseline MCH.
    • A minimal model (M1) using only demographics and clinical history showed robust performance (AUC 0.82).
    • Longitudinal models accurately predicted time-to-MCH onset (R²=0.68) and stratified four-year risk, identifying a transient vascular instability phenotype linked to hepatic factors.

    Conclusions:

    • MCH-Guard provides a flexible clinical decision-support tool for optimizing MCH and ARIA surveillance.
    • The high-performing clinical-only M1 model facilitates equitable risk assessment in resource-limited settings.
    • Characterization of vascular instability addresses a critical confounder in safety monitoring for anti-amyloid therapies.