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Updated: Sep 5, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Monitoring mild cognitive impairment of workers exposed to occupational aluminium based on quantitative
Z Y Zhang1, H R Jiang1, X R Sun1
1College of Medical Imaging, Shanxi Medical University, Taiyuan 030001, Shanxi Province, China.
Aim:
To investigate the diagnostic value of quantitative susceptibility mapping (QSM) in mild cognitive impairment (MCI) of aluminium (Al) workers.
Materials And Methods:
The basic data of 53 workers in an Al factory were collected and divided into the MCI group and normal control (NC) group by Montreal Cognitive Assessment (MoCA) scores. All participants were tested for plasma Al concentration and had magnetic resonance imaging (MRI). The QSM values of many areas of the brain were delineated and measured. Independent two-sample t-tests or non-parametric tests were used to compare the parameter values between the two groups. Spearman's correlation analysis was performed between QSM values, MoCA scores, and plasma Al concentration. The receiver operating characteristic curve and z test were performed to assess diagnostic efficacy and the best parameter.
Results:
There was no difference in age and educational level. Plasma Al concentration of the MCI group was higher than that of NC group (p=0.057). QSM values of the left hippocampus, left dentate nucleus, right substantia nigra, and left putamen in MCI group were higher than that of NC group (p<0.05), and the left hippocampus had the best diagnostic efficacy. QSM values correlated negatively with MoCA scores. No correlation was found between QSM values and plasma Al concentration (p>0.05).
Conclusion:
QSM might be a neuroimaging marker for the diagnosis of MCI. The left hippocampus showed the best diagnostic efficacy. Plasma Al concentration of the MCI group was higher than that of the NC group. A correlation between QSM and plasma Al concentration was not found.
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