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Related Experiment Video

Updated: Jan 7, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

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Clinical Manifestations.

Alice Stanton1, Claire L Lancaster2, Jennifer M Rusted3

  • 1University of Sussex, Brighton, East Sussex, United Kingdom.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 25, 2025
PubMed
Summary
This summary is machine-generated.

Novel Verbal Fluency Task (VFT) scoring methods, both manual and automated, can detect early neurocognitive changes missed by traditional assessments. These advanced techniques improve early dementia risk identification.

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Area of Science:

  • Neuroscience
  • Cognitive Psychology
  • Computational Linguistics

Background:

  • Early detection of neurodegenerative diseases like Alzheimer's is critical for timely intervention.
  • Traditional Verbal Fluency Task (VFT) scoring may not capture subtle preclinical cognitive changes.
  • Advanced VFT scoring methods offer potential for enhanced early detection of dementia risk.

Purpose of the Study:

  • To systematically review and evaluate nuanced manual and automated Verbal Fluency Task (VFT) scoring methods.
  • To assess the utility of advanced VFT metrics (e.g., clustering, switching) for identifying individuals at risk of future dementia.
  • To synthesize findings on the effectiveness of novel VFT scoring in preclinical neurocognitive assessment.

Main Methods:

  • Systematic literature search of PubMed, PsycINFO, and Scopus databases (inception to Sept 2024) following PRISMA guidelines.
  • Inclusion criteria: automated or non-traditional manual VFT scoring in adults with Alzheimer's disease, Mild Cognitive Impairment, biomarker positivity, or APOE4 variant.
  • Exclusion criteria: studies using only traditional VFT scoring or acoustic speech features; narrative synthesis of findings.

Main Results:

  • Fifty-three studies were included, with 41.5% using automated and 58.5% novel manual scoring methods.
  • Advanced semantic clustering (e.g., Word2Vec) and linguistic features (word frequency, typicality) improved early cognitive impairment detection and diagnostic accuracy.
  • Longitudinal data indicated automated clustering enhanced predictive power for dementia over 9 years; increased word typicality correlated with MCI to AD transition risk.

Conclusions:

  • Both automated and novel manual VFT analyses demonstrate significant promise for detecting early cognitive decline.
  • Automated methods offer speed and consistency; manual methods provide interpretability and flexibility.
  • Future research should prioritize developing transparent, validated, and clinically integrated automated VFT tools through co-design for diverse populations.