| Abstrakt: |
Predicting the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is critical for early intervention. Towards this end, various deep learning models have been applied in this domain, typically relying on structural magnetic resonance imaging (sMRI) data from a single time point whereas neglecting the dynamic changes in brain structure over time. Current longitudinal studies inadequately explore disease evolution dynamics and are burdened by high computational complexity. This paper introduces a novel lightweight 3D convolutional neural network specifically designed to capture the evolution of brain diseases for modeling the progression of MCI. First, a longitudinal lesion feature selection strategy is proposed to extract core features from temporal data, facilitating the detection of subtle differences in brain structure between two time points. Next, to refine the model for a more concentrated emphasis on lesion features, a disease trend attention mechanism is introduced to learn the dependencies between overall disease trends and local variation features. Finally, disease prediction visualization techniques are employed to improve the interpretability of the final predictions. Extensive experiments demonstrate that the proposed model achieves state-of-the-art performance in terms of area under the curve (AUC), accuracy, specificity, precision, and F1 score. This study confirms the efficacy of our early diagnostic method, utilizing only two follow-up sMRI scans to predict the disease status of MCI patients 24 months later with an AUC of 79.03%. |
