ArticleProceedings. IEEE International Symposium on Biomedical Imaging2019
EARLY PREDICTION OF ALZHEIMER'S DISEASE DEMENTIA BASED ON BASELINE HIPPOCAMPAL MRI AND 1-YEAR FOLLOW-UP COGNITIVE MEASURES USING DEEP RECURRENT NEURAL NETWORKS.
Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled 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
- Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine.Psychoradiology · 2026Review
- Longitudinal structural MRI-based deep learning and radiomics features for predicting Alzheimer's disease progression.Alzheimer's research & therapy · 2025Article
- Review of multimodal machine learning approaches in healthcare.An international journal on information fusion · 2025Article
- Editorial: Spatiotemporal & AI trends in neuroscience, neuroimaging, and neurooncology.Frontiers in neuroimaging · 2025Article
- Multimodality Fusion Aspects of Medical Diagnosis: A Comprehensive Review.Bioengineering (Basel, Switzerland) · 2024Review
- Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging.NPJ digital medicine · 2024Review
- TA-RNN: an attention-based time-aware recurrent neural network architecture for electronic health records.Bioinformatics (Oxford, England) · 2024Article
- Advancing healthcare through multimodal data fusion: a comprehensive review of techniques and applications.PeerJ. Computer science · 2024Article
- PPAD: a deep learning architecture to predict progression of Alzheimer's disease.Bioinformatics (Oxford, England) · 2023Article
- Multimodal fusion models for pulmonary embolism mortality prediction.Scientific reports · 2023Article
- Deep Learning of Videourodynamics to Classify Bladder Dysfunction Severity in Patients With Spina Bifida.The Journal of urology · 2023Article
- Predicting Alzheimer's Disease and Quantifying Asymmetric Degeneration of the Hippocampus Using Deep Learning of Magnetic Resonance Imaging Data.Proceedings. IEEE International Symposium on Biomedical Imaging · 2023Article
- Article
- PPAD: A deep learning architecture to predict progression of Alzheimer's disease.bioRxiv : the preprint server for biology · 2023Article
- Hippocampal representations for deep learning on Alzheimer's disease.Scientific reports · 2022Article
- Supervised Computer-Aided Diagnosis (CAD) Methods for Classifying Alzheimer's Disease-Based Neurodegenerative Disorders.Computational and mathematical methods in medicine · 2022Article
- Toward attention-based learning to predict the risk of brain degeneration with multimodal medical data.Frontiers in neuroscience · 2022Article
- DeepAtrophy: Teaching a neural network to detect progressive changes in longitudinal MRI of the hippocampal region in Alzheimer's disease.NeuroImage · 2021Article
- Applications of artificial intelligence in drug development using real-world data.Drug discovery today · 2021Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Multi-modal biological, imaging, and neuropsychological markers have demonstrated promising performance for distinguishing Alzheimer's disease (AD) patients from cognitively normal elders. However, it remains difficult to early predict when and which mild cognitive impairment (MCI) individuals will convert to AD dementia. Informed by pattern classification studies which have demonstrated that pattern classifiers built on longitudinal data could achieve better classification performance than those built on cross-sectional data, we develop a deep learning model based on recurrent neural networks (RNNs) to learn informative representation and temporal dynamics of longitudinal cognitive measures of individual subjects and combine them with baseline hippocampal MRI for building a prognostic model of AD dementia progression. Experimental results on a large cohort of MCI subjects have demonstrated that the deep learning model could learn informative measures from longitudinal data for characterizing the progression of MCI subjects to AD dementia, and the prognostic model could early predict AD progression with high accuracy.
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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.