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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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Related Experiment Video

Updated: Jan 7, 2026

Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies
07:20

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Biomarkers.

Jin Sung Kim1, Whani Kim2, Hyun Jeong Ko2

  • 1Sangmyung University, Seoul, Jongno-gu, Korea, Republic of (South).

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

Eye movement analysis can detect amyloid-beta (Aβ) pathology in Alzheimer's disease (AD) using novel metrics and smartphone technology. This offers a cost-effective method for early Aβ detection, aiding timely intervention.

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Area of Science:

  • Ophthalmology and Neuroscience
  • Biomarker Discovery
  • Medical Diagnostics

Background:

  • Amyloid-beta (Aβ) accumulation is a key Alzheimer's disease (AD) biomarker, typically detected via expensive PET scans.
  • Eye movement analysis presents a cost-effective, non-invasive alternative for Aβ detection.
  • Previous eye-tracking studies faced challenges in distinguishing Aβ+ from Aβ- individuals, particularly with post-stimulus data.

Purpose of the Study:

  • To develop and validate an eye movement analysis method for differentiating Aβ+ and Aβ- individuals.
  • To introduce novel eye movement parameters, Self-Correction Time (SCT) and Gaze Instability, for improved diagnostic accuracy.
  • To assess the efficacy of these parameters combined with the Return to Central Fixation (RCF) phase in a mobile setting.

Main Methods:

  • 193 participants (aged 50+) were classified as Aβ+ or Aβ- using PET imaging.
  • Participants performed anti-saccade tasks using a smartphone-based eye-tracking application.
  • Analysis included classic features, novel SCT and Gaze Instability metrics, and the RCF phase, evaluated by a machine learning model.

Main Results:

  • The CatBoost model achieved high classification performance (AUC 0.83, sensitivity 0.85, specificity 0.82) when integrating all features (Classic + PS + RCF).
  • Significant differences in AUC were observed across various feature combinations, highlighting the impact of novel parameters and RCF.
  • The study demonstrated the effectiveness of SCT and Gaze Instability in differentiating Aβ groups.

Conclusions:

  • Redefined self-correction parameters (SCT) and Gaze Instability metrics, alongside the RCF phase, enhance measurement precision in mobile eye-tracking.
  • This approach provides an economical method for detecting amyloid pathology, potentially enabling earlier diagnosis of preclinical AD.
  • The findings support the use of smartphone-based eye tracking for expedited clinical intervention and improved AD management.