ArticleFrontiers in dementia2023
Prediction of Progression from Mild Cognitive Impairment to Alzheimer's disease with Longitudinal and Multimodal Data.
Article in Frontiers in dementia, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Unawareness of deficits in mild cognitive impairment: a systematic review of its role in progression to Alzheimer's disease.BMC neurology · 2026Pooled it
- Prediction of cognitive decline and Alzheimer's disease conversion by a plasma biomarker panel in non-demented individuals.GeroScience · 2026Article
- Explainable Machine Learning Reveals Time-Dependent Cognitive Risk in Minor Neurocognitive Disorder: Implications for Health Promotion and Early Risk Stratification.Biomedicines · 2026Article
- Editorial: Mild cognitive impairment and cognitive aging.Frontiers in behavioral neuroscience · 2026Article
- Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data.IEEE reviews in biomedical engineering · 2026Review
- Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions.Acta pharmaceutica Sinica. B · 2025Review
- Article
- Plasma GFAP and Amyloid Pathology Predict Cognitive Response to Multidomain Interventions in MCI.Aging and disease · 2025Observational
- Association of the digital clock drawing test with amyloid and tau PET biomarkers in low age risk adults.Scientific reports · 2025Article
- Psychometric Properties of a Neuropsychological Assessment Scale for Diagnosing Alzheimer's Disease in the Illiterate and Low-Educated Subjects in Iran.Iranian journal of psychiatry · 2025Article
- Shape modeling of longitudinal medical images: from diffeomorphic metric mapping to deep learning.Frontiers in artificial intelligence · 2025Review
- Predicting cognitive change using functional, structural, and neuropsychological predictors.Brain communications · 2025Article
- Article
- Exploring the Perspectives of Older Adults on a Digital Brain Health Platform Using Natural Language Processing: Cohort Study.JMIR formative research · 2024Article
- Advanced AI techniques for classifying Alzheimer's disease and mild cognitive impairment.Frontiers in aging neuroscience · 2024Article
- Combining artificial neural networks and a marginal structural model to predict the progression from depression to Alzheimer's disease.Frontiers in dementia · 2024Article
- Predicting and Evaluating Cognitive Status in Aging Populations Using Decision Tree Models.American journal of Alzheimer's disease and other dementiasArticle
- Early Alzheimer's disease patient care pathway and health system readiness: A framework for integrated care.Alzheimer's & dementia (New York, N. Y.)Article
Corrections and comments
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Authors and funding
8 authors.
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Abstract
Introduction: Accurate prediction of the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) within a certain time frame is crucial for appropriate therapeutic interventions. However, it is challenging to capture the dynamic changes in cognitive and functional abilities over time, resulting in limited predictive performance. Our study aimed to investigate whether incorporating longitudinal multimodal data with advanced analytical methods could improve the capability to predict the risk of progressing to AD. Methods: This study included participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI), a large-scale multi-center longitudinal study. Three data modalities, including demographic variables, neuropsychological tests, and neuroimaging measures were considered. A Long Short-Term Memory (LSTM) model using data collected at five-time points (baseline, 6-month, 12-month, 18-month, and 24-month) was developed to predict the risk of progression from MCI to AD within two years from the index exam (the exam at 24-month). In contrast, a random forest model was developed to predict the risk of progression just based on the data collected at the index exam. Results: The study included 347 participants with MCI at 24-month (age: mean 75, SD 7 years; 39.8% women) from ADNI, of whom 77 converted to AD over a 2-year follow-up period. The longitudinal LSTM model showed superior prediction performance of MCI-to-AD progression (AUC 0.93±0.06) compared to the random forest model (AUC 0.90±0.09). A similar pattern was also observed across different age groups. Discussion: Our study suggests that the incorporation of longitudinal data can provide better predictive performance for 2-year MCI-to-AD progression risk than relying solely on cross-sectional data. Therefore, repeated or multiple times routine health surveillance of MCI patients are essential in the early detection and intervention of AD.
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Registered trials
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