ArticleScientific reports2019
Predicting Alzheimer's disease progression using multi-modal deep learning approach.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 138 papers, 7 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
138 citing papers in PubMed, 7 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
- Limited generalizability and high risk of bias in multivariable models predicting conversion risk from mild cognitive impairment to dementia: A systematic review.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Pooled it
- Predictive models of Alzheimer's disease dementia risk in older adults with mild cognitive impairment: a systematic review and critical appraisal.BMC geriatrics · 2024Pooled it
- Whole-genome sequencing reveals novel ethnicity-specific rare variants associated with Alzheimer's disease.Molecular psychiatry · 2022Pooled it
- Prediction Models for Conversion From Mild Cognitive Impairment to Alzheimer's Disease: A Systematic Review and Meta-Analysis.Frontiers in aging neuroscience · 2022Pooled 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
- Ethnic and trans-ethnic genome-wide association studies identify new loci influencing Japanese Alzheimer's disease risk.Translational psychiatry · 2021Pooled it
- A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer's disease.Scientific reports · 2021Trial
- 2025 Advances in Brain Health: Brain Health as Capital for Vascular and Cognitive Resilience.Stroke · 2026Article
- Longitudinal multi-modal data prediction model for mild cognitive impairment by deep survival analysis.BMC medical informatics and decision making · 2026Article
- Artificial neural networks fighting real neural decline: a systematic review of AI in Alzheimer's research.Artificial intelligence review · 2026Article
- AI-enabled multimodal neuroimaging for neurotransmitter mapping in normal aging and age-related disease.Frontiers in aging neuroscience · 2026Review
- Robust disease prognosis via diagnostic knowledge preservation: A sequential learning approach.PloS one · 2026Article
- Intra-scale interaction and cross-scale fusion network for detecting the progression of neurodegeneration in Alzheimer's disease.Scientific reports · 2025Article
- Decoding the genomic symphony: unravelling brain disorders through data integration and machine learning.Molecular psychiatry · 2025Review
- Enhanced EfficientNet-Extended Multimodal Parkinson's disease classification with Hybrid Particle Swarm and Grey Wolf Optimizer.Scientific reports · 2025Article
- Robust Disease Prognosis via Diagnostic Knowledge Preservation: A Sequential Learning Approach.medRxiv : the preprint server for health sciences · 2025Article
- Development and validation of a model to predict the progression of Alzheimer's disease.Age and ageing · 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
- Whole-genome sequencing reveals the impact of lipid pathway and APOE genotype on brain amyloidosis.Human molecular genetics · 2025Article
78 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative condition marked by a decline in cognitive functions with no validated disease modifying treatment. It is critical for timely treatment to detect AD in its earlier stage before clinical manifestation. Mild cognitive impairment (MCI) is an intermediate stage between cognitively normal older adults and AD. To predict conversion from MCI to probable AD, we applied a deep learning approach, multimodal recurrent neural network. We developed an integrative framework that combines not only cross-sectional neuroimaging biomarkers at baseline but also longitudinal cerebrospinal fluid (CSF) and cognitive performance biomarkers obtained from the Alzheimer's Disease Neuroimaging Initiative cohort (ADNI). The proposed framework integrated longitudinal multi-domain data. Our results showed that 1) our prediction model for MCI conversion to AD yielded up to 75% accuracy (area under the curve (AUC) = 0.83) when using only single modality of data separately; and 2) our prediction model achieved the best performance with 81% accuracy (AUC = 0.86) when incorporating longitudinal multi-domain data. A multi-modal deep learning approach has potential to identify persons at risk of developing AD who might benefit most from a clinical trial or as a stratification approach within clinical trials.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.