MCI Conversion Prediction Based on Transfer Learning

Abstract

Amnestic mild cognitive impairment (MCI) commonly represents an intermediate stage situated in the spectrum between normal age-related cognitive decline and dementia. Predicting of MCI conversion to Alzheimer's Disease (AD) plays critical roles in early diagnosis and disease-modifying therapies. We analyzed baseline 3T MRI scans in 337 MCI patients from the ADNI-GO and ANDI-2 cohorts. The subjects were divided into MCI non-converters (MCInc) and MCI converters (MCIc). To evaluate conversion rates, we aim to first extract intermediate representations of structural MRI (sMRI) by a pre-trained convolutional neural network (CNN) model, then combine principal component analysis (PCA) and sequential feature selection (SFS) for feature selection, and finally adopt support vector machine (SVM) for prediction. The method attained an accuracy of 77.58%, a sensitivity of 90.48%, a specificity of 76.42%, which may be useful and practical for clinical diagnosis.