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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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A Machine Learning Framework for Cognitive Impairment Screening from Speech with Multimodal Large Models.

Shiyu Chen1, Ying Tan1, Wenyu Hu2

  • 1Laboratory of Research and Translation for Geriatric Diseases, Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Youyi Road, Yuzhong District, Chongqing 400016, China.

Bioengineering (Basel, Switzerland)
|January 28, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an AI-powered speech analysis tool for early Alzheimer's disease (AD) detection. The novel framework accurately identifies cognitive impairment using voice data, offering a scalable and non-invasive screening solution.

Keywords:
Alzheimer’s diseaseacoustic feature extractiondigital biomarkersearly diagnosismachine learning

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Area of Science:

  • Neurology
  • Artificial Intelligence
  • Speech Science

Background:

  • Early Alzheimer's disease (AD) diagnosis is crucial for managing cognitive decline.
  • Current diagnostic methods are often invasive, costly, and time-consuming, hindering widespread screening.
  • There is a significant need for accessible, non-invasive, and scalable screening tools for AD.

Purpose of the Study:

  • To develop and validate a novel AI-driven framework for early Alzheimer's disease detection using speech analysis.
  • To assess the efficacy of machine learning models in classifying cognitive states based on speech features.
  • To identify key speech characteristics indicative of early-stage cognitive impairment.

Main Methods:

  • A multimodal large language model combined with structured speech tasks (AAM-MMSE) was used to collect voice data from 1098 participants.
  • Speaker embeddings, speech labels, and acoustic features were extracted using CosyVoice2 and converted into statistical representations.
  • Fourteen machine learning models were trained for classification into Healthy Control (HC), Mild Cognitive Impairment (MCI), and Alzheimer's Disease (AD), with SHAP analysis for feature importance.

Main Results:

  • LightGBM and Gradient Boosting classifiers achieved the highest performance with an average AUC of 0.9501.
  • SHAP analysis identified spectral complexity, energy dynamics, and temporal features as critical for distinguishing cognitive states.
  • These findings align with known speech alterations in early AD, confirming the model's relevance.

Conclusions:

  • The proposed framework provides a non-invasive, interpretable, and scalable method for cognitive screening.
  • Speech-based AI models show significant potential for early AD detection.
  • The system is suitable for both clinical settings and telemedicine applications.