Deep Learning-Based Assessment of Cognitive Impairment in Elderly Using General Blood Test

Abstract

Background: Development of a mass screening test for cognitive impairment is important for preventing onset of dementia. Based on the results of previous studies that cognitive impairment may be caused by systemic disorders, we developed a method of early detection using a deep neural network (DNN) trained on blood test data from health examinations. Methods: We studied a total of 304 subjects (age 72.1± 12.8 years); the cognitive function was assessed by the Mini Mental State Examination (MMSE), while systemic disorders were evaluated by a general blood test. First, we developed trained a DL-based algorithm DNN for predicting the subject's MMSE score based on the blood test data and age of subject in the algorism group in 202 patients with systemic disorders (73.5 ± 13.1years). The accuracy of the trained DL model DNN was first evaluated by leave-one-out cross validation Leave-one-out Cross Validation (LOO CV) in the Algorism group. Next, the trained DL model DNN was validated in the test a second group including 65 patients with systemic disorders (73.6 ± 11.0 year) and 37 healthy subjects (62.0 ± 8.6 years). Results: In the Training Set's LOO CV the leave-one out cross validation, the predicted MMSE scores exhibited a significant positive correlation with ground truth (the measured) -MMSE scores (p<0.001). The trained DNN model exhibited a both high prediction sensitivity and (90%) and specificity scores of (90% ) for two-class binary classification (23/24 cut off) (normal ≥ 24, cognitive impairment ≤23). In Finally, for the Out-of-Sample Set, upon which no training was performed, Test-group, the predicted MMSE scores exhibited a significant positive correlation with the ground truth measured MMSE scores (p<0.001). The DNN model exhibited a high prediction accuracy for the two-classbinary classification, with a sensitivity of 75% and a specificity of 87%. Interpretation: The DL-based algorithm DNN predicted cognitive impairment expressed by MMSE scores with high accuracy. Going forward, The DL-based such AI-based assessment of cognitive impairment may become a mass screening test of cognitive impairment, and contribute to prevention strategies for dementia. Funding: This work was supported in part by Grant-in-Aids from the MEXT (S1411017), JSPS Grant-in-Aid for Young Scientists (B, 16K16077). Declaration of Interest: There are no competing interests in this study. Ethical Approval: The ethical committee in the hospital approved this study.