ArticlePloS one2017
Prediction and classification of Alzheimer disease based on quantification of MRI deformation.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 3 of them syntheses that pooled it.
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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.
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Who cites it
53 citing papers in PubMed, 3 syntheses or guidelines pooled it, 177 citations in OpenAlex.
- Evaluating the Therapeutic Effects of Oxytocin on Animal Model of Alzheimer's Disease: A Systematic Review.Current Alzheimer research · 2025Pooled it
- Transfer Learning for Alzheimer's Disease through Neuroimaging Biomarkers: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled 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
- Artificial Intelligence for Alzheimer's Disease Diagnosis: From Traditional Machine Learning to Large Language Models.Biosensors · 2026Review
- Diagnostic Accuracy of MRI-Based Artificial Intelligence Models for Distinguishing Alzheimer's Disease and Mild Cognitive Impairment From Cognitively Normal Controls: A Systematic Review and Meta-Analysis.Dementia and neurocognitive disorders · 2026Review
- Deep learning models identify brain changes during the progression of Alzheimer's disease.NPJ systems biology and applications · 2026Article
- Vision and convolutional transformers for Alzheimer's disease diagnosis: a systematic review of architectures, multimodal fusion and critical gaps.Brain informatics · 2025Review
- A hybrid transformer-based approach for early detection of Alzheimer's disease using MRI images.BioImpacts : BI · 2025Article
- Exploring the relationship among Alzheimer's disease, aging and cognitive scores through neuroimaging-based approach.Scientific reports · 2024Article
- Early Alzheimer's Disease Detection: A Review of Machine Learning Techniques for Forecasting Transition from Mild Cognitive Impairment.Diagnostics (Basel, Switzerland) · 2024Review
- Prediction of Alzheimer's disease progression within 6 years using speech: A novel approach leveraging language models.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Deep Learning for Alzheimer's Disease Prediction: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2024Review
- Association of retinal optical coherence tomography metrics and polygenic risk scores with cognitive function and future cognitive decline.The British journal of ophthalmology · 2024Article
- Deep learning-based Alzheimer's disease detection: reproducibility and the effect of modeling choices.Frontiers in computational neuroscience · 2024Article
- A Review on the Use of Modern Computational Methods in Alzheimer's Disease-Detection and Prediction.Current Alzheimer research · 2024Review
- OViTAD: Optimized Vision Transformer to Predict Various Stages of Alzheimer's Disease Using Resting-State fMRI and Structural MRI Data.Brain sciences · 2023Article
- Convolution Neural Networks and Self-Attention Learners for Alzheimer Dementia Diagnosis from Brain MRI.Sensors (Basel, Switzerland) · 2023Article
- CAD-ALZ: A Blockwise Fine-Tuning Strategy on Convolutional Model and Random Forest Classifier for Recognition of Multistage Alzheimer's Disease.Diagnostics (Basel, Switzerland) · 2023Article
- Differences in macular vessel density in the superficial plexus across cognitive impairment: the NORFACE cohort.Scientific reports · 2022Article
- A multi-expert ensemble system for predicting Alzheimer transition using clinical features.Brain informatics · 2022Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Detecting early morphological changes in the brain and making early diagnosis are important for Alzheimer's disease (AD). High resolution magnetic resonance imaging can be used to help diagnosis and prediction of the disease. In this paper, we proposed a machine learning method to discriminate patients with AD or mild cognitive impairment (MCI) from healthy elderly and to predict the AD conversion in MCI patients by computing and analyzing the regional morphological differences of brain between groups. Distance between each pair of subjects was quantified from a symmetric diffeomorphic registration, followed by an embedding algorithm and a learning approach for classification. The proposed method obtained accuracy of 96.5% in differentiating mild AD from healthy elderly with the whole-brain gray matter or temporal lobe as region of interest (ROI), 91.74% in differentiating progressive MCI from healthy elderly and 88.99% in classifying progressive MCI versus stable MCI with amygdala or hippocampus as ROI. This deformation-based method has made full use of the pair-wise macroscopic shape difference between groups and consequently increased the power for discrimination.
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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.