ArticleScientific reports2017
Longitudinal measurement and hierarchical classification framework for the prediction of Alzheimer's disease.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it, 55 citations in OpenAlex.
- Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment.The Cochrane database of systematic reviews · 2020Pooled it
- Explainable machine learning for predicting longitudinal dementia status: Establishing a leakage-free benchmark.PLOS digital health · 2026Article
- DYNAMIC PREDICTION WITH MULTIVARIATE LONGITUDINAL OUTCOMES AND LONGITUDINAL MAGNETIC RESONANCE IMAGING DATA.The annals of applied statistics · 2025Article
- Comparison of Ensemble Techniques for Early Prediction of Alzhiemer Disease.Research square · 2024Article
- Multivariate functional mixed model with MRI data: An application to Alzheimer's disease.Statistics in medicine · 2023Article
- X chromosome-wide association study of quantitative biomarkers from the Alzheimer's Disease Neuroimaging Initiative study.Frontiers in aging neuroscience · 2023Article
- An interpretable Alzheimer's disease oligogenic risk score informed by neuroimaging biomarkers improves risk prediction and stratification.Frontiers in aging neuroscience · 2023Article
- A classification for complex imbalanced data in disease screening and early diagnosis.Statistics in medicine · 2022Article
- Graph Models of Pathology Spread in Alzheimer's Disease: An Alternative to Conventional Graph Theoretic Analysis.Brain connectivity · 2021Review
- An atlas of classifiers-a machine learning paradigm for brain MRI segmentation.Medical & biological engineering & computing · 2021Article
- Early MCI-to-AD Conversion Prediction Using Future Value Forecasting of Multimodal Features.Computational intelligence and neuroscience · 2021Article
- Using machine learning to quantify structural MRI neurodegeneration patterns of Alzheimer's disease into dementia score: Independent validation on 8,834 images from ADNI, AIBL, OASIS, and MIRIAD databases.Human brain mapping · 2020Article
- Incorporating spatial-anatomical similarity into the VGWAS framework for AD biomarker detection.Bioinformatics (Oxford, England) · 2019Article
- A collection input based support tensor machine for lesion malignancy classification in digital breast tomosynthesis.Physics in medicine and biology · 2019Article
- Article
- EARLY PREDICTION OF ALZHEIMER'S DISEASE DEMENTIA BASED ON BASELINE HIPPOCAMPAL MRI AND 1-YEAR FOLLOW-UP COGNITIVE MEASURES USING DEEP RECURRENT NEURAL NETWORKS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2019Article
- Predicting Alzheimer's disease progression using multi-modal deep learning approach.Scientific reports · 2019Article
- MildInt: Deep Learning-Based Multimodal Longitudinal Data Integration Framework.Frontiers in genetics · 2019Article
- Quantifying Neurodegenerative Progression With DeepSymNet, an End-to-End Data-Driven Approach.Frontiers in neuroscience · 2019Article
- Spatial correlations exploitation based on nonlocal voxel-wise GWAS for biomarker detection of AD.NeuroImage. Clinical · 2019Article
Corrections and comments
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Authors and funding
5 authors at 20 institutions in 3 countries.
Funding
Abstract
Accurate prediction of Alzheimer's disease (AD) is important for the early diagnosis and treatment of this condition. Mild cognitive impairment (MCI) is an early stage of AD. Therefore, patients with MCI who are at high risk of fully developing AD should be identified to accurately predict AD. However, the relationship between brain images and AD is difficult to construct because of the complex characteristics of neuroimaging data. To address this problem, we present a longitudinal measurement of MCI brain images and a hierarchical classification method for AD prediction. Longitudinal images obtained from individuals with MCI were investigated to acquire important information on the longitudinal changes, which can be used to classify MCI subjects as either MCI conversion (MCIc) or MCI non-conversion (MCInc) individuals. Moreover, a hierarchical framework was introduced to the classifier to manage high feature dimensionality issues and incorporate spatial information for improving the prediction accuracy. The proposed method was evaluated using 131 patients with MCI (70 MCIc and 61 MCInc) based on MRI scans taken at different time points. Results showed that the proposed method achieved 79.4% accuracy for the classification of MCIc versus MCInc, thereby demonstrating very promising performance for AD prediction.
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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.