ArticleAlzheimer's & dementia (Amsterdam, Netherlands)2018
Fusion of deep learning models of MRI scans, Mini-Mental State Examination, and logical memory test enhances diagnosis of mild cognitive impairment.
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 38 papers, 2 of them syntheses that pooled it.
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Who cites it
38 citing papers in PubMed, 2 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
- Transfer Learning for Alzheimer's Disease through Neuroimaging Biomarkers: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer's disease.Scientific reports · 2021Trial
- Weakly Supervised MRI-Based Classification of Alzheimer's Disease Using Clinical Pseudo-Labels.NeuroSci · 2026Article
- Mild cognitive impairment cases affect the predictive power of Alzheimer's disease diagnostic models using routine clinical variables.npj aging · 2026Article
- X-ViTCNN: A Novel Network-Level Fusion of Transfer Learning and Customized Vision Transformer for Multi-Stage Alzheimer's Disease Prediction Using MRI Scans.Diagnostics (Basel, Switzerland) · 2026Article
- TriFusion-ADFormer: a deep learning framework for early Alzheimer's disease detection using MRI and cognitive metrics.Frontiers in artificial intelligence · 2026Article
- Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine.Psychoradiology · 2026Review
- Multimodal classification of mood disorders using structural MRI and polygenic risk scores: a machine learning ablation study in UK Biobank.Frontiers in psychiatry · 2026Article
- Vision and convolutional transformers for Alzheimer's disease diagnosis: a systematic review of architectures, multimodal fusion and critical gaps.Brain informatics · 2025Review
- Multimodal data fusion in neuroscience: promises, challenges and future directions.IEEE signal processing magazine · 2025Article
- Prediction of Mini-Mental State Examination Scores for Cognitive Impairment and Machine Learning Analysis of Oral Health and Demographic Data Among Individuals Older Than 60 Years: Cross-Sectional Study.JMIR medical informatics · 2025Article
- Review of multimodal machine learning approaches in healthcare.An international journal on information fusion · 2025Article
- An empirical study of using radiology reports and images to improve intensive care unit mortality prediction.JAMIA open · 2025Article
- A novel MRI-based deep learning-radiomics framework for evaluating cerebrospinal fluid signal in central nervous system infection.Frontiers in medicine · 2025Article
- HiMAL: Multimodal Hierarchical Multi-task Auxiliary Learning framework for predicting Alzheimer's disease progression.JAMIA open · 2024Article
- Multimodality Fusion Strategies in Eye Disease Diagnosis.Journal of imaging informatics in medicine · 2024Article
- Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging.NPJ digital medicine · 2024Review
- Disease-driven domain generalization for neuroimaging-based assessment of Alzheimer's disease.Human brain mapping · 2024Article
- Machine learning with multimodal neuroimaging data to classify stages of Alzheimer's disease: a systematic review and meta-analysis.Cognitive neurodynamics · 2024Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
introductionOur aim was to investigate if the accuracy of diagnosing mild cognitive impairment (MCI) using the Mini-Mental State Examination (MMSE) and logical memory (LM) test could be enhanced by adding MRI data.
methodsData of individuals with normal cognition and MCI were obtained from the National Alzheimer Coordinating Center database (n = 386). Deep learning models trained on MRI slices were combined to generate a fused MRI model using different voting techniques to predict normal cognition versus MCI. Two multilayer perceptron (MLP) models were developed with MMSE and LM test results. Finally, the fused MRI model and the MLP models were combined using majority voting.
resultsThe fusion model was superior to the individual models alone and achieved an overall accuracy of 90.9%. DISCUSSION: This study is a proof of principle that multimodal fusion of models developed using MRI scans, MMSE, and LM test data is feasible and can better predict MCI.
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