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Clinical Manifestations.

Greta Keller1, Laouen Belloli2, Lara Gauder2

  • 1Fleni, Buenos Aires, Argentina.

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

Artificial Intelligence (AI) tools analyzing speech and facial cues can effectively detect amnestic mild cognitive impairment (aMCI). This multimodal approach shows high diagnostic accuracy, aiding early intervention for aMCI.

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Early detection of amnestic mild cognitive impairment (aMCI) is critical for timely interventions.
  • Artificial Intelligence (AI) offers novel methods for identifying aMCI markers, though research is nascent.
  • This study focuses on AI-driven multimodal instruments for distinguishing aMCI from healthy cognition, emphasizing local data calibration.

Purpose of the Study:

  • To design and validate AI-powered multimodal neuropsychological instruments.
  • To differentiate individuals with aMCI from healthy controls using AI markers.
  • To assess the efficacy of AI in analyzing language, speech, and facial expressions for aMCI detection.

Main Methods:

  • Recruited 59 participants (30 healthy, 29 aMCI) from Argentina.
  • Collected data via neuropsychological tests, MRI, and video/audio recordings of language tasks.
  • Extracted multimodal markers from language processing, speech acoustics, facial analysis, and emotion recognition.

Main Results:

  • AI markers showed significant correlations with traditional neurocognitive tests (47%).
  • Univariate analysis yielded above-chance diagnostic performance for aMCI (AUC 0.57).
  • A multivariate random forest classifier integrating all modalities achieved high diagnostic accuracy (AUC 0.91).

Conclusions:

  • AI-based multimodal markers effectively differentiate aMCI from healthy controls in an Argentine population.
  • The study highlights the potential of non-invasive, cost-effective AI tools for aMCI detection.
  • Validation with Spanish-language data is crucial for broader clinical application.