A revolutionary Cornell-led study uses machine learning to understand the advancement of Alzheimer's disease in people who are either cognitively normal or experiencing mild cognitive impairment. The machine learning modelling showed that predicting the future decline into dementia for individuals with mild cognitive impairment is easier and more accurate than for cognitively normal or asymptomatic individuals. Researchers also found that the predictions for cognitively normal subjects are less accurate for longer time horizons, but the opposite is true for individuals with mild cognitive impairment. Cornell researchers also found that magnetic resonance imaging (MRI) is a useful prognostic tool for people in both stages. In contrast, tools that track molecular biomarkers, such as positron emission tomography (PET) scans, are more useful for people experiencing mild cognitive impairment.

A Cornell University-led collaboration has used machine learning to pinpoint the most accurate means and timelines for anticipating the advancement of Alzheimer's disease in people who are either cognitively normal or experiencing mild cognitive impairment. The modeling showed that predicting the future decline into dementia for individuals with mild cognitive impairment is easier and more accurate than it is for cognitively normal (or asymptomatic) individuals. At the same time, the researchers found that the predictions for cognitively normal subjects is less accurate for longer time horizons, but for individuals with mild cognitive impairment, the opposite is true. The modeling also demonstrated that magnetic resonance imaging (MRI) is a useful prognostic tool for people in both stages, whereas tools that track molecular biomarkers, such as positron emission tomography (PET) scans, are more useful for people experiencing mild cognitive impairment. The team's paper, "Machine Learning Based Multi-Modal Prediction of Future Decline Toward Alzheimer's Disease: An Empirical Study," published Nov. 16 in PLOS ONE.