AI-Based Forecasting of Mild Cognitive Impairment to Alzheimer's Disease Using Multi-Modal Approach

Abstract

With no medication currently available and a clinical trial failure rate of 99.6% for Alzheimer’s disease (AD) , early diagnosis is crucial to prevent its progression. MCI has been identified as a transitional stage between healthy aging and AD, making it promising for early detection. In this study, we propose a machine learning (ML) based survival analysis approach to predict the time to AD conversion in early MCI and late MCI stages separately, as we found that the progression rate varies in both stages. Unlike typical ML classifiers, ML-based survival analysis models can provide information about the timing and likelihood of disease progression. We employed multiple ML survival models, including Random Survival Forest (RSF), Extra Survival Trees (XST), Gradient Boosting Survival Analysis (GB), Survival Tree (ST), Cox-net, and Cox Proportional Hazard (CoxPH), on 291 eMCI and 546 lMCI subjects. The study also compared different data modalities, such as cognitive tests, neuroimaging tests, and cerebrospinal fluid (CSF) biomarkers, both individually and in combination to identify the most influential features for the models' performance. The results show that RSF outperformed traditional CoxPH and other ML models used in this study. For the eMCI dataset, RSF trained on multimodal data achieved a C-Index of 0.96 and an IBS of 0.02. For the lMCI dataset, the C-Index was 0.82 and the IBS was 0.16. Additionally, the multimodal analysis highlighted the importance of cognitive tests, as they exhibited a statistically significant improvement over other modalities and multimodal data, demonstrating their reliability in predicting AD progression. Finally, individual survival curves were generated using RSF on baseline data to predict the probability of early onset of AD in patients. This facilitates clinical decision-making by assisting clinicians in developing personalized treatment strategies and implementing preventive measures to slow down or potentially stop the progression of AD during its early stages.