ArticleFrontiers in aging neuroscience2022
A Two-Stage Model for Predicting Mild Cognitive Impairment to Alzheimer's Disease Conversion.
Article in Frontiers in aging neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 29 citations in OpenAlex.
- Predicting Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using a Vision Transformer and Hippocampal MRI Slices.Bioengineering (Basel, Switzerland) · 2026Article
- Predicting progression of Alzheimer's disease using blood-based multi-omics data.Bioinformatics advances · 2026Article
- The application of AI-driven digital phenotyping and interventions in neuropsychiatric disorders.Frontiers in psychiatry · 2026Review
- Multimodal Self-Supervised Learning for Early Alzheimer's: Cross-Modal MRI-PET, Longitudinal Signals, and Site Invariance.Diagnostics (Basel, Switzerland) · 2025Article
- Explainable Machine Learning Models for Brain Diseases: Insights from a Systematic Review.Neurology international · 2024Review
- Early Alzheimer's Disease Detection: A Review of Machine Learning Techniques for Forecasting Transition from Mild Cognitive Impairment.Diagnostics (Basel, Switzerland) · 2024Review
- Machine learning with multimodal neuroimaging data to classify stages of Alzheimer's disease: a systematic review and meta-analysis.Cognitive neurodynamics · 2024Review
- Predicting long-term progression of Alzheimer's disease using a multimodal deep learning model incorporating interaction effects.Journal of translational medicine · 2024Article
- Deep CNN ResNet-18 based model with attention and transfer learning for Alzheimer's disease detection.Frontiers in neuroinformatics · 2024Article
- Self-Supervised Contrastive Learning to Predict the Progression of Alzheimer's Disease with 3D Amyloid-PET.Bioengineering (Basel, Switzerland) · 2023Article
- Article
- Addressing Mild Cognitive Impairment and Boosting Wellness for the Elderly through Personalized Remote Monitoring.Healthcare (Basel, Switzerland) · 2022Article
- Deep learning interpretability in neuroimaging: A comprehensive survey and methodological recommendations.Imaging neuroscience (Cambridge, Mass.)Article
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early detection of Alzheimer's disease (AD), such as predicting development from mild cognitive impairment (MCI) to AD, is critical for slowing disease progression and increasing quality of life. Although deep learning is a promising technique for structural MRI-based diagnosis, the paucity of training samples limits its power, especially for three-dimensional (3D) models. To this end, we propose a two-stage model combining both transfer learning and contrastive learning that can achieve high accuracy of MRI-based early AD diagnosis even when the sample numbers are restricted. Specifically, a 3D CNN model was pretrained using publicly available medical image data to learn common medical features, and contrastive learning was further utilized to learn more specific features of MCI images. The two-stage model outperformed each benchmark method. Compared with the previous studies, we show that our model achieves superior performance in progressive MCI patients with an accuracy of 0.82 and AUC of 0.84. We further enhance the interpretability of the model by using 3D Grad-CAM, which highlights brain regions with high-predictive weights. Brain regions, including the hippocampus, temporal, and precuneus, are associated with the classification of MCI, which is supported by the various types of literature. Our model provides a novel model to avoid overfitting because of a lack of medical data and enable the early detection of AD.
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