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Mild Cognitive Impairment Detection System Based on Unstructured Spontaneous Speech: Longitudinal Dual-Modal

Yu-Shan Liao1, Thiri Wai2, Ting-Yun Liao1

  • 1Graduate Institute of Networking and Multimedia, National Taiwan University, Taipei, Taiwan.

JMIR Medical Informatics
|January 15, 2026
PubMed
Summary

This study introduces a novel dual-modal system using autobiographical memory (AM) speech data for early detection of mild cognitive impairment (MCI). The system tracks cognitive changes over time, improving diagnostic accuracy for this aging-related condition.

Keywords:
Alzheimer disease detectionautobiographical memory testdeep learninglongitudinal speech analysismild cognitive impairmentmultimodal fusion

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Gerontology

Background:

  • Cognitive diseases, including Alzheimer's, are increasing with global population aging.
  • Mild cognitive impairment (MCI) is a critical transitional stage requiring early diagnosis to slow disease progression.
  • Early detection of MCI is vital for timely treatment and managing healthcare costs.

Purpose of the Study:

  • To develop a dual-modal longitudinal cognitive detection system for MCI using autobiographical memory (AM) speech data.
  • To enhance the accuracy of MCI detection by analyzing both speech and text data.
  • To track cognitive changes over time in spontaneous speech through an aging trajectory module.

Main Methods:

  • Utilized autobiographical memory (AM) test speech data for a dual-modal analysis (speech and text).
  • Introduced an aging trajectory module with local and global alignment loss functions to capture time-related cognitive changes.
  • Developed a longitudinal detection system to monitor cognitive status over multiple time points.

Main Results:

  • The longitudinal model with the aging trajectory module achieved AUCs of 0.85 and 0.89 on two Chinese datasets.
  • Demonstrated significant improvement over cross-sectional, single time point models.
  • Validated the model's generalizability on the ADReSSo dataset, achieving accuracy over 0.88.

Conclusions:

  • Presented a noninvasive, scalable approach for early MCI detection using longitudinal AM speech data.
  • The dual-modal system with an aging trajectory module effectively captures cognitive decline trends.
  • The method shows robustness and generalizability for real-world, long-term cognitive monitoring.