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

Topic: Risk, prevention and care in population studies

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 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 (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.

Key findings

• 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%.

Why it matters

This deep learning framework was designed to predict progression from mild cognitive impairment to Alzheimer's disease using longitudinal structural MRI scans. It is a prediction tool only and does not test any treatment, cure or prevention.

Study details

• Study designlongitudinal study
• Effect size81.69%; 80.27%; 84.35%
• Evidence strengthModerate

Read the original

• DOI: 10.1109/access.2025.3562432
• Original source →
• Free full text: IEEE Access →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{zheng2025-a879517e,
  author  = {Zheng, Guojiang and Lü, Yang and Chen, Hui},
  title   = {{Deep Learning-Based Framework for Predicting Mild Cognitive Impairment Progression in Neurology Using Longitudinal MRI}},
  journal = {IEEE Access},
  year    = {2025},
  doi     = {10.1109/access.2025.3562432},
  url     = {https://doi.org/10.1109/access.2025.3562432}
}

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

• Detailed comparison of amyloid PET and CSF biomarkers for identifying early Alzheimer disease Neurology · 2015-09-09 · usefulness score 72/100 · Biomarkers
• NIA-AA Research Framework: Toward a biological definition of Alzheimer's disease Alzheimer's & dementia : the journal of the Alzheimer's Association · 2018-04-01 · usefulness score 64/100 · Getting a diagnosis
• 2013 Alzheimer's disease facts and figures Alzheimer's & dementia : the journal of the Alzheimer's Association · 2013-03-01 · usefulness score 62/100 · How common it is
• Accuracy of Biomarker Testing for Neuropathologically Defined Alzheimer Disease in Older Adults With Dementia Annals of internal medicine · 2020-04-28 · usefulness score 61/100 · Biomarkers

Related studies in Mild cognitive impairment

• Predicting Mild Cognitive Impairment to Alzheimer's Disease Progression by Explainable Multi‐Modal Framework Using Deep and Machine Learning Hybrid Model International Journal of Imaging Systems and Technology · 2026-06-09 · usefulness score 35/100
• Longitudinal structural MRI-based deep learning and radiomics features for predicting Alzheimer’s disease progression Alzheimer s Research & Therapy · 2025-08-07 · usefulness score 55/100
• Advancing Early Diagnosis: Predicting Mild Cognitive Impairment Progression in Normal Individuals Using Deep Learning on MRI Features IEEE Access · 2025-01-01 · usefulness score 34/100
• Comparison of Deep Learning and Traditional Machine Learning Models for Predicting Mild Cognitive Impairment Using Plasma Proteomic Biomarkers International Journal of Molecular Sciences · 2025-03-08 · usefulness score 48/100
• Predicting Alzheimer's disease progression using multi-modal deep learning approach Scientific reports · 2019-02-13 · usefulness score 66/100
• Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification PloS one · 2016-02-22 · usefulness score 55/100

Similar studies

• Longitudinal Structural MRI-Based Deep Learning And Radiomics Features For Predicting Alzheimer\\\\\\\'s Disease Progression 2026 · Brain scans and imaging
• Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data 2019 · Digital health tools
• Predicting the progression of mild cognitive impairment using machine learning: A systematic, quantitative and critical review 2021 · Digital health tools
• Explainable machine learning for predicting longitudinal dementia status: Establishing a leakage-free benchmark 2026 · Digital health tools
• A longitudinal analysis of T1-weighted MRI features associated with progression from mild cognitive impairment to Alzheimer’s disease 2026 · Mild cognitive impairment
• Machine learning diagnosis of cognitive impairment and dementia in harmonized older adult cohorts 2025 · Digital health tools

More on Mild cognitive impairment

• A review that covers this ground Risk factors for predicting progression from mild cognitive impairment to Alzheimer's disease: a systematic review and meta-analysis of cohort studies Journal of neurology, neurosurgery, and psychiatry · 2015-05-22 · usefulness score 75/100
• Newest in this subject Paths of Aging Minds: Health Journeys of Older Adults with Mild Cognitive Impairment Research Square · 2026-11-01 · Preprint — not yet peer-reviewed · usefulness score 16/100
• Newest in this subject Neuroinflammation is an early pathological process in a mouse model of prodromal Parkinsons disease bioRxiv · 2026-10-30 · Preprint — not yet peer-reviewed · usefulness score 16/100

See all studies in Mild cognitive impairment →