ArticleAlzheimer's & dementia (Amsterdam, Netherlands)2018
Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review.
Article in Alzheimer's & dementia (Amsterdam, Netherlands), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 136 papers, 5 of them syntheses that pooled it.
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Who cites it
136 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Deep learning for MRI-based acute and subacute ischaemic stroke lesion segmentation-a systematic review, meta-analysis, and pilot evaluation of key results.Frontiers in medical technology · 2025Pooled it
- Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer's Disease Not Using Neuroimaging Biomarkers: A Systematic Review.Journal of Alzheimer's disease : JAD · 2024Pooled 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
- Application of Support Vector Machine on fMRI Data as Biomarkers in Schizophrenia Diagnosis: A Systematic Review.Frontiers in psychiatry · 2020Pooled it
- Automated detection of age-related macular degeneration in color fundus photography: a systematic review.Survey of ophthalmologyPooled it
- A Flexible-Horizon Clinical Decision Support Model for Dementia Early Detection and Risk Prediction in Secondary Care: A Responsible AI Approach.Journal of medical systems · 2026Article
- Artificial Intelligence and Digital Biomarkers for Early Detection and Monitoring of Neurological Disorders: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Centralized Review of Alzheimer's Disease and Related Dementias Biomedical Repositories and Computational Methods.Bioengineering (Basel, Switzerland) · 2026Review
- Age-Related Differences in Neural Networks for Error Detection and Inhibitory Control: A LORETA-Based Comparative Study.Brain sciences · 2026Article
- Amyloid-beta statuses prediction with free water MR imaging features in Alzheimer's disease using machine learning models.BMC medical imaging · 2026Article
- Adaptive neighborhood aggregation graph neural network for early diagnosis of Alzheimer's disease.Scientific reports · 2026Article
- Mild cognitive impairment cases affect the predictive power of Alzheimer's disease diagnostic models using routine clinical variables.npj aging · 2026Article
- Imaging-genetics-based dementia risk prediction using deep survival neural networks in the Rotterdam Study.Scientific reports · 2026Article
- Bidirectional prognostic predictions of conversion and reversion in amnestic mild cognitive impairment.GeroScience · 2026Article
- Predicting the progression of MCI and Alzheimer's disease on structural brain integrity and other features with machine learning.GeroScience · 2026Article
- Multimodal MRI to elucidate cross-organ coupling mechanisms of the heart-brain axis.Frontiers in cardiovascular medicine · 2026Review
- Ultra-fast MRI for brain-age prediction in a real-world cognitive disorders clinic.Frontiers in aging neuroscience · 2026Article
- Quantifying White Matter Hyperintensities: Automated Volumetry Compared with Visual Grading Scales.Medicina (Kaunas, Lithuania) · 2025Article
- Vision and convolutional transformers for Alzheimer's disease diagnosis: a systematic review of architectures, multimodal fusion and critical gaps.Brain informatics · 2025Review
- Gaining Brain Insights by Tapping into the Black Box: Linking Structural MRI Features to Age and Cognition using Shapley-Based Interpretation Methods.Neuroinformatics · 2025Article
76 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
introductionAdvanced machine learning methods might help to identify dementia risk from neuroimaging, but their accuracy to date is unclear.
methodsWe systematically reviewed the literature, 2006 to late 2016, for machine learning studies differentiating healthy aging from dementia of various types, assessing study quality, and comparing accuracy at different disease boundaries.
resultsOf 111 relevant studies, most assessed Alzheimer's disease versus healthy controls, using AD Neuroimaging Initiative data, support vector machines, and only T1-weighted sequences. Accuracy was highest for differentiating Alzheimer's disease from healthy controls and poor for differentiating healthy controls versus mild cognitive impairment versus Alzheimer's disease or mild cognitive impairment converters versus nonconverters. Accuracy increased using combined data types, but not by data source, sample size, or machine learning method. DISCUSSION: Machine learning does not differentiate clinically relevant disease categories yet. More diverse data sets, combinations of different types of data, and close clinical integration of machine learning would help to advance the field.
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