Two-stage deep learning model for Alzheimer’s disease detection and prediction of the mild cognitive impairment time

Alzheimer’s disease (AD) is an irreversible neurodegenerative disease characterized by thinking, behavioral and memory impairments. Early prediction of conversion from mild cognitive impairment (MCI) to AD is still a challenging task. No study has been able to predict the exact conversion time of MCI patients. In addition, most studies have achieved poor performance making this prediction using only a small number of features (e.g., using only MRI images). Therefore, previous approaches have not gained the trust of medical experts. This study proposes a novel two-stage deep learning AD progression detection framework based on information fusion of several patient longitudinal multivariate modalities, including neuroimaging data, cognitive scores, cerebrospinal fluid biomarkers, neuropsychological battery markers, and demographics. The first stage of the progression detection framework employs a multiclass classification task that predicts a patient’s diagnosis (i.e., cognitively normal, MCI, or AD). In the second stage, a regression task that predicts the exact conversion time of MCI patients is used. The study is based on data of 1,371 subjects collected by the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Comprehensive experiments were carried out to evaluate the framework stages and find the optimal model for each stage. Proposed model was compared with various machine learning models, including decision tree (DT), random forest (RF), support vector machine (SVM), logistic regression (LR), and K-nearest neighbor (KNN). In the classification stage, the proposed long-short term memory (LSTM) model achieved an accuracy of 93.87%, precision of 94.070%, recall of 94.07%, and F1-score of 94.07%. The results showed that the LSTM model outperformed other machine learning models (i.e., decision tree by 2.48%, random forest by 1.27%, support vector machine by 1.86%, logistic regression by 1.59%, and K-nearest neighbor by 14.77%). In the regression stage, the proposed LSTM model achieved the best results (i.e., mean absolute error of 0.1375). Compared to other regular regressors, this LSTM model achieved less errors (i.e., 0.0064, 0.0152, 0.0338, 0.0118, 0.0198, and 0.0066, compared to DT, RF, SVM, LR, and KNN, respectively). By learning deep representation from patient high-dimensional longitudinal time-series data, the proposed LSTM model was more stable and medically acceptable. The framework may have a clinical impact as a predictive tool for AD progression detection due to its accurate results to predict the exact conversion time of MCI cases using patient time-series multimodalities data.