Conclusions
AD dementia, MCI
CORTEX
This is the first study to apply machine learning and statistical models to recruit patients for external validation of the olfactory-stimulated fNIRS diagnostic technique using a previous statistical model. 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/or AD dementia compared to the classic statistical model. Our results suggest that the machine learning algorithm is stable across different patient groups and increases generalization and reproducibility. We suggest that this machine learning model with a novel fNIRS approach can be used as a potential diagnostic tool for patients with MCI and/or AD dementia.