Trial reportScientific reports2018
Machine Learning-based Individual Assessment of Cortical Atrophy Pattern in Alzheimer's Disease Spectrum: Development of the Classifier and Longitudinal Evaluation.
Trial report in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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
32 citing papers in PubMed, 57 citations in OpenAlex.
- Alzheimer's disease-like brain pattern biomarker: capturing risks and predicting disease onset.Molecular psychiatry · 2026Article
- Use of Artificial Intelligence for the Early Detection and Prediction of Alzheimer's Disease.Cureus · 2026Review
- Alzheimer's Disease-Like Brain Pattern Biomarker: Capturing Risks and Predicting Disease Onset.Research square · 2025Article
- UnCOT-AD: Unpaired Cross-Omics Translation Enables Multi-Omics Integration for Alzheimer's Disease Prediction.Briefings in bioinformatics · 2025Article
- Volume and Permeability of White Matter Hyperintensity on Cognition: A DCE Imaging Study of an Older Cohort With and Without Cognitive Impairment.Journal of magnetic resonance imaging : JMRI · 2025Article
- Deep learning to predict rapid progression of Alzheimer's disease from pooled clinical trials: A retrospective study.PLOS digital health · 2024Article
- Deep learning-based quantification of brain atrophy using 2D T1-weighted MRI for Alzheimer's disease classification.Frontiers in aging neuroscience · 2024Article
- A Two-Year Observational Study to Evaluate Conversion Rates from High- and Low-Risk Patients with Amnestic Mild Cognitive Impairment to Probable Alzheimer's Disease in a Real-World Setting.Journal of Alzheimer's disease reports · 2024Article
- Deep learning model for individualized trajectory prediction of clinical outcomes in mild cognitive impairment.Frontiers in aging neuroscience · 2024Article
- Leveraging Technology to Diagnose Alzheimer's Disease: A Systematic Review and Meta-Analysis.Healthcare (Basel, Switzerland) · 2023Review
- From genetic correlations of Alzheimer's disease to classification with artificial neural network models.Functional & integrative genomics · 2023Article
- Comparison of Normative Percentiles of Brain Volume Obtained from NeuroQuantJournal of the Korean Society of Radiology · 2023Article
- Development and validation of a deep learning-based automatic segmentation model for assessing intracranial volume: comparison with NeuroQuant, FreeSurfer, and SynthSeg.Frontiers in neurology · 2023Article
- Research on nonstroke dementia screening and cognitive function prediction model for older people based on brain atrophy characteristics.Brain and behavior · 2022Article
- Agreement and Reliability between Clinically Available Software Programs in Measuring Volumes and Normative Percentiles of Segmented Brain Regions.Korean journal of radiology · 2022Article
- Cognitive Sequelae and Hippocampal Dysfunction in Chronic Kidney Disease following 5/6 Nephrectomy.Brain sciences · 2022Article
- Magnetic susceptibility in the deep gray matter may be modulated by apolipoprotein E4 and age with regional predilections: a quantitative susceptibility mapping study.Neuroradiology · 2022Article
- Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer's Disease.Life (Basel, Switzerland) · 2022Review
- Harmonization of Multicenter Cortical Thickness Data by Linear Mixed Effect Model.Frontiers in aging neuroscience · 2022Article
- Monitoring Alzheimer's Disease Progression in Mild Cognitive Impairment Stage Using Machine Learning-Based FDG-PET Classification Methods.Journal of Alzheimer's disease : JAD · 2022Article
Corrections and comments
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Authors and funding
12 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To develop a new method for measuring Alzheimer's disease (AD)-specific similarity of cortical atrophy patterns at the individual-level, we employed an individual-level machine learning algorithm. A total of 869 cognitively normal (CN) individuals and 473 patients with probable AD dementia who underwent high-resolution 3T brain MRI were included. We propose a machine learning-based method for measuring the similarity of an individual subject's cortical atrophy pattern with that of a representative AD patient cohort. In addition, we validated this similarity measure in two longitudinal cohorts consisting of 79 patients with amnestic-mild cognitive impairment (aMCI) and 27 patients with probable AD dementia. Surface-based morphometry classifier for discriminating AD from CN showed sensitivity and specificity values of 87.1% and 93.3%, respectively. In the longitudinal validation study, aMCI-converts had higher atrophy similarity at both baseline (p < 0.001) and first year visits (p < 0.001) relative to non-converters. Similarly, AD patients with faster decline had higher atrophy similarity than slower decliners at baseline (p = 0.042), first year (p = 0.028), and third year visits (p = 0.027). The AD-specific atrophy similarity measure is a novel approach for the prediction of dementia risk and for the evaluation of AD trajectories on an individual subject level.
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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.