Deep Learning-Based Framework for Predicting Mild Cognitive Impairment Progression in Neurology Using Longitudinal MRI

Deep Learning-Based Framework for Predicting Mild Cognitive Impairment Progression in Neurology Using Longitudinal MRI

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Alzheimer’s disease (AD), a leading neurodegenerative disorder, progresses from an intermediary stage known as Mild Cognitive Impairment (MCI), characterized by measurable cognitive decline with retained functional independence.Accurate prediction of MCI progression to AD is critical for timely interventions.Existing deep learning-based methods for structural MRI (sMRI) analysis predominantly utilize either Convolutional Neural Networks (CNNs), which effectively capture local features but neglect global context, or Transformer architectures that model global dependencies yet require extensive data and computational resources.Additionally, many methods inadequately leverage longitudinal imaging data, limiting their sensitivity to subtle temporal changes in brain morphology.To overcome these limitations, we introduce EffiSwin-MCI, a novel hybrid deep learning framework integrating EfficientNet and natio celebrate eyeshadow palette Swin Transformer architectures, specifically designed for longitudinal sMRI analysis.

The primary novelty of EffiSwin-MCI lies in its sliding-window attention mechanism, inspired by the Swin Transformer, which effectively integrates localized spatial dependencies within 2D sMRI slices, combined with temporal attention blocks that fuse spatial-temporal features across longitudinal scans at two distinct time points us polo assn mens sweaters (T1 and T2).EfficientNet-B2 serves as a computationally efficient backbone, extracting hierarchical spatial features crucial for detailed morphological characterization.This alternating spatial and temporal attention strategy uniquely captures progressive local and global structural changes indicative of cognitive decline.Comprehensive experiments conducted on the Alzheimer’s Disease ADNI dataset demonstrate the proposed model’s superior performance compared to state-of-the-art CNN and Transformer-based approaches, achieving an accuracy of 81.69%, recall of 80.

27%, precision of 84.35%, and F1-score of 82.27%.EffiSwin-MCI’s interpretability is further validated through Grad-CAM visualizations, highlighting critical neurodegenerative biomarkers such as the hippocampus and amygdala, reinforcing its clinical relevance for early prediction and intervention strategies in MCI management.