ArticleFrontiers in neuroscience2018
Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer's Disease Prediction From Mild Cognitive Impairment.
Article in Frontiers in neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 105 papers, 6 of them syntheses that pooled it.
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Who cites it
105 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- Convolutional neural network models of structural MRI for discriminating categories of cognitive impairment: a systematic review and meta-analysis.BMC neurology · 2025Pooled it
- Magnetic resonance imaging-based machine learning classification of schizophrenia spectrum disorders: a meta-analysis.Psychiatry and clinical neurosciences · 2024Pooled it
- Predictive modelling of brain disorders with magnetic resonance imaging: A systematic review of modelling practices, transparency, and interpretability in the use of convolutional neural networks.Human brain mapping · 2023Pooled it
- Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review.Alzheimer's research & therapy · 2021Pooled it
- Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment.The Cochrane database of systematic reviews · 2020Pooled it
- Actigraphy Meets AI: A Digital Biomarker for Parkinson's Disease and Isolated REM Sleep Behaviour Disorder.Journal of sleep research · 2026Article
- Learning heritable multimodal brain representation via contrastive learning.Research square · 2026Article
- Next generation preventive neurology: how artificial intelligence and machine learning are reshaping Alzheimer's disease research.Behavioral and brain functions : BBF · 2026Review
- Learning heritable multimodal brain representation via contrastive learning.bioRxiv : the preprint server for biology · 2026Article
- Deep learning approaches to map individual differences in macroscopic neural structure with variations in spatial navigation behavior.Neuropsychologia · 2026Article
- MRI neuroimaging-based Alzheimer's disease stage classification using deep neural network with convolutional block attention module and GAN-style noise injection.Scientific reports · 2026Article
- Normalizing flow based neural processes for Alzheimer's disease progression prediction.PloS one · 2026Article
- Early detection of Alzheimer's disease progression: comparative evaluation of deep learning models.Scientific reports · 2025Article
- Integration of Genetic and Imaging Data for Alzheimer's Disease Diagnosis and Interpretation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Deep Learning-Based Pediatric Brain Region Segmentation and Volumetric Analysis for General Growth Pattern in Healthy Children.Journal of imaging informatics in medicine · 2025Article
- Deep learning-based CAD system for Alzheimer's diagnosis using deep downsized KPLS.Scientific reports · 2025Article
- Unveiling the potential of artificial intelligence in revolutionizing disease diagnosis and prediction: a comprehensive review of machine learning and deep learning approaches.European journal of medical research · 2025Review
- MssNet: An Efficient Spatial Attention Model for Early Recognition of Alzheimer's Disease.IEEE transactions on emerging topics in computational intelligence · 2025Article
- Machine-learning models for Alzheimer's disease diagnosis using neuroimaging data: survey, reproducibility, and generalizability evaluation.Brain informatics · 2025Review
45 more citing papers are in PubMed but not listed here.
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Authors and funding
11 authors.
Funding
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Abstract
Mild cognitive impairment (MCI) is the prodromal stage of Alzheimer's disease (AD). Identifying MCI subjects who are at high risk of converting to AD is crucial for effective treatments. In this study, a deep learning approach based on convolutional neural networks (CNN), is designed to accurately predict MCI-to-AD conversion with magnetic resonance imaging (MRI) data. First, MRI images are prepared with age-correction and other processing. Second, local patches, which are assembled into 2.5 dimensions, are extracted from these images. Then, the patches from AD and normal controls (NC) are used to train a CNN to identify deep learning features of MCI subjects. After that, structural brain image features are mined with FreeSurfer to assist CNN. Finally, both types of features are fed into an extreme learning machine classifier to predict the AD conversion. The proposed approach is validated on the standardized MRI datasets from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project. This approach achieves an accuracy of 79.9% and an area under the receiver operating characteristic curve (AUC) of 86.1% in leave-one-out cross validations. Compared with other state-of-the-art methods, the proposed one outperforms others with higher accuracy and AUC, while keeping a good balance between the sensitivity and specificity. Results demonstrate great potentials of the proposed CNN-based approach for the prediction of MCI-to-AD conversion with solely MRI data. Age correction and assisted structural brain image features can boost the prediction performance of CNN.
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