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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Deep Learning-Based Screening Test for Cognitive Impairment Using Basic Blood Test Data for Health Examination.

Kaoru Sakatani1,2, Katsunori Oyama3, Lizhen Hu1

  • 1Department of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan.

Frontiers in Neurology
|December 31, 2020
PubMed
Summary

This study developed a deep learning model using blood tests to estimate cognitive function, showing accurate predictions for various groups. The model shows promise for early detection of cognitive impairment, especially in aging populations.

Keywords:
Alzheimer's diseaseMini Mental State Examinationartificial intelligencedeep leaningdementiascreening testvascular cognitive impairment

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Area of Science:

  • Artificial Intelligence in Medicine
  • Neurology
  • Biomarkers

Background:

  • Cognitive impairment screening is crucial for early intervention.
  • Systemic metabolic disorders can impact cognitive function.
  • Developing non-invasive screening methods is a priority.

Purpose of the Study:

  • To develop and validate a deep learning model for estimating cognitive function using basic blood test data.
  • To assess the model's accuracy in predicting cognitive impairment across different patient and healthy groups.
  • To explore the potential of blood-based biomarkers for cognitive health assessment.

Main Methods:

  • A deep neural network (DNN) was trained on 23 blood test items and age data from 202 patients with metabolic disorders.
  • The DNN model's predictive performance was validated using Mini-Mental State Examination (MMSE) scores from three groups: stroke patients, healthy individuals, and health examination participants.
  • Cognitive function was assessed using the MMSE, with predictions compared against ground truth scores.

Main Results:

  • The DNN model demonstrated significant positive correlations between predicted and actual MMSE scores in patient and healthy groups (r=0.66, p<0.001).
  • No significant difference was found between predicted and actual MMSE scores in the patient group.
  • In healthy and health examination groups, predicted MMSE scores were slightly lower than ground truth, suggesting potential for early detection of cognitive decline.

Conclusions:

  • The developed DNN model accurately predicts cognitive function from basic blood test data.
  • The model's ability to identify subtle cognitive differences in healthy individuals highlights its potential for early cognitive impairment screening.
  • The findings suggest that this approach may predict future cognitive decline associated with aging and atherosclerosis.