Predicting conversion from MCI to AD with FDG-PET brain images at different prodromal stages
We study the prediction of MCI to AD conversion using FDG-PET and several classifiers. We study how the disease stage impacts diagnostic performance. Results show a decrease in performance with the temporal distance to conversion. We achieve an accuracy of 85.1% at the time of conversion and 75% 24 months before it.
conversion fdg-pet alzheimer?s disease mild cognitive impairment early diagnosis machine learning