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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.

Mingxia Wei1, Jincheng Li2,3, Nathan Zhao1

  • 1Huashan Hospital, Fudan University, Shanghai, Shanghai, China.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 24, 2025
PubMed
Summary
This summary is machine-generated.

An AI eye-tracking system offers a scalable solution for early cognitive impairment (CI) detection. This digital biomarker approach shows high accuracy in identifying CI, aiding prompt diagnosis and resource allocation.

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Biomarkers

Background:

  • Aging populations necessitate early detection of cognitive impairment (CI) to mitigate dementia burden.
  • Current CI diagnosis in China is hindered by time-intensive, subjective assessments, particularly in underserved regions.
  • Digital biomarkers, specifically eye movement features, present an accessible and objective diagnostic avenue.

Purpose of the Study:

  • To develop and validate an AI-based eye-tracking system for efficient and objective CI screening.
  • To establish a feasible framework for CI management as a governmental priority.
  • To explore the utility of eye movement features as digital biomarkers for CI detection.

Main Methods:

  • Analysis of data from outpatient, inpatient, and community cohorts (N=3338 total).
  • Selection of optimal eye movement features and machine learning algorithms for an eye-tracking model.
  • Validation of the model in outpatient, inpatient, and diverse community settings, including association analysis with neuroimaging data.

Main Results:

  • The eye-tracking model achieved high diagnostic accuracy (AUC=0.986, SN=91.3%, SP=96.4%) in internal validation for CI detection.
  • Key eye movement features correlated significantly with neuropsychological scores and brain atrophy.
  • External validation in community cohorts demonstrated good generalizability, with high-risk individuals showing elevated cognitive decline indicators.

Conclusions:

  • The AI-based eye-tracking system shows significant potential as a preliminary screening tool for CI.
  • This technology can enhance early diagnosis, facilitate timely treatment, and optimize medical resource allocation.
  • Eye movement analysis offers a scalable, objective, and accessible method for CI detection.