A Multimodal Machine Learning Model for Predicting Dementia Conversion in Alzheimer’s Disease

Abstract

Abstract Alzheimer's disease (AD) accounts for 60–70% of the population with dementia. Despite the integration of MRI and PET in AD clinics and research, there is a lack of validated models for predicting dementia conversion from MCI. Thus, we aimed to investigate and validate a machine learning model to predict this. A total of 196 subjects were enrolled from four hospitals and the Alzheimer’s Disease Neuroimaging Initiative dataset. Volumes of the ROI, white matter hyperintensity, and regional SUVR were analyzed using T1, T2-FLAIR MRIs, and amyloid PET (αPET), along with automatically provided hippocampal occupancy scores and Fazekas scales. Compared with the GBM model trained solely on demographics, AUC of the cross-validation models incorporating T1 image features (pBonferroni=0.03) and T1 and αPET image features (pBonferroni<0.001). The two cross-validated models (pBonferroni=0.08) did not differ significantly in their predictive measures. After performing the inference, the model combining T1 and αPET image features exhibited the highest AUC (0.875), which was comparable to that of the model using only T1 image features (0.835). Our machine learning model utilizing Solitaire T1 MRI features shows promising predictive value for dementia conversion within a 4-year timeframe, making it applicable in circumstances where αPET is unavailable.