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Blood Studies for Cardiovascular System I: Cardiac Biomarkers01:20

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Cardiac biomarkers are enzymes, proteins, and hormones released into the blood when cardiac cells are injured. They are powerful tools for triaging.
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Cardiac biomarkers are critical in diagnosing, prognosing, and managing cardiovascular diseases. Routine measurement of specific biomarkers such as B-type natriuretic peptide (BNP), C-reactive protein (CRP), and homocysteine (Hcy) is common practice in clinical settings to evaluate heart function and predict cardiovascular events.
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Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies
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Biomarkers.

Gauri Darekar1, Taslim Murad1, Hui-Yuan Miao1

  • 1Washington University in St. Louis, St. Louis, MO, USA.

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Summary
This summary is machine-generated.

This study used artificial intelligence (AI) to predict brain age from MRI scans, identifying key brain regions linked to normal aging and Alzheimer's disease (AD). The AI model accurately predicted brain age and revealed patterns associated with AD severity.

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence
  • Gerontology

Background:

  • Age is a major risk factor for mild cognitive impairment and Alzheimer's disease (MCI/AD).
  • Understanding normal brain aging and MCI/AD requires identifying brain age patterns.
  • Previous research focused on individual brain regions, neglecting multivariate associations.

Purpose of the Study:

  • To develop and validate an AI model for predicting brain age using MRI data.
  • To identify significant multivariate brain regions associated with brain age prediction.
  • To explore the relationship between brain age patterns and MCI/AD clinical severity.

Main Methods:

  • Utilized deep learning (AgeNet) and machine learning models for brain age estimation from regional brain volumes.
  • Integrated the optimal AI model with SHAP for identifying key multivariate brain regions.
  • Validated the methodology on simulated and experimental MRI datasets (n=668).

Main Results:

  • The deep learning model (AgeNet) significantly outperformed conventional models in brain age prediction.
  • AgeNet-SHAP successfully identified key predictors of brain age in simulations.
  • Significant, widespread regional differences in brain structure were observed in Alzheimer's disease patients compared to controls.

Conclusions:

  • Explainable AI (AgeNet-SHAP) effectively predicts brain age and reveals multivariate brain region associations.
  • The method identified brain regions associated with Alzheimer's disease severity.
  • These findings support AI-driven approaches for diagnostics, prognostics, and personalized medicine in neurodegenerative diseases.