Main findings
dementia, AD dementia, MCI, cognitive impairment
CORTEX
Through two independent trials, we found that machine learning models using olfactory-stimulated oxygenation differences in the orbitofrontal cortex were superior in diagnosing MCI and AD dementia compared to the classic statistical model. In this study, we presented two machine learning models for the classification of MCI-AD dementia and CN and for the classification of MCI and CN. Our models used an ensemble approach to combine state-of-the-art models.For the classification of MCI-AD dementia and CN, we combined four models, the GB and LGB, which provided AUC values of 0.925 and 0.825 for the original and additional trial datasets, respectively. For the classification of MCI and CN, we utilized single model LGB, which provided AUC values of 0.860 and 0.854 for the original and additional trial data, respectively. Compared to the classic statistical approach published in the previous study, our model provided consistent performance regardless of different datasets and higher AUC values. In particular, fNIRS, which is a useful diagnostic method, is the top contributor for both classification AI-driven models. Our results provide quantification of cognitive impairment (MCI and/or AD dementia) using olfactory-stimulated fNIRS with machine learning to improve generalization and reproducibility.