ArticleFrontiers in aging neuroscience2022
Deep Learning Model for Prediction of Progressive Mild Cognitive Impairment to Alzheimer's Disease Using Structural MRI.
Article in Frontiers in aging neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
15 citing papers in PubMed.
- Resource-stratified machine learning framework for cognitive status classification and mild cognitive impairment to dementia progression prediction.Alzheimer's research & therapy · 2026Article
- Multi-Architecture Deep Learning for Early Alzheimer's Detection in MRI: Slice- and Scan-Level Analysis.International journal of environmental research and public health · 2026Article
- Predicting the progression of MCI and Alzheimer's disease on structural brain integrity and other features with machine learning.GeroScience · 2026Article
- Multi-Task Deep Learning Model for Automated Detection and Severity Grading of Lumbar Spinal Stenosis on MRI: Multi-Center External Validation.Diseases (Basel, Switzerland) · 2026Article
- PredatorNet: a clinically trustworthy deep learning framework for multi-class Alzheimer's disease classification using MRI.Frontiers in aging neuroscience · 2026Article
- An interpretable multimodal ensemble assessment framework for Alzheimer's disease cognitive staging.Frontiers in neurology · 2026Article
- IoMT driven Alzheimer's prediction model empowered with transfer learning and explainable AI approach in healthcare 5.0.Scientific reports · 2025Article
- Fractional gradient optimized explainable convolutional neural network for Alzheimer's disease diagnosis.Heliyon · 2024Article
- Deep Ensemble learning and quantum machine learning approach for Alzheimer's disease detection.Scientific reports · 2024Article
- Comparing a pre-defined versus deep learning approach for extracting brain atrophy patterns to predict cognitive decline due to Alzheimer's disease in patients with mild cognitive symptoms.Alzheimer's research & therapy · 2024Article
- Optimizing Machine Learning Models for Accessible Early Cognitive Impairment Prediction: A Novel Cost-effective Model Selection Algorithm.IEEE access : practical innovations, open solutions · 2024Article
- Vision transformer-equipped Convolutional Neural Networks for automated Alzheimer's disease diagnosis using 3D MRI scans.Frontiers in neurology · 2024Article
- PINNet: a deep neural network with pathway prior knowledge for Alzheimer's disease.Frontiers in aging neuroscience · 2023Article
- Vision transformers for the prediction of mild cognitive impairment to Alzheimer's disease progression using mid-sagittal sMRI.Frontiers in aging neuroscience · 2023Article
- Conventional machine learning and deep learning in Alzheimer's disease diagnosis using neuroimaging: A review.Frontiers in computational neuroscience · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease (AD) is an irreversible neurological disorder that affects the vast majority of dementia cases, leading patients to experience gradual memory loss and cognitive function decline. Despite the lack of a cure, early detection of Alzheimer's disease permits the provision of preventive medication to slow the disease's progression. The objective of this project is to develop a computer-aided method based on a deep learning model to distinguish Alzheimer's disease (AD) from cognitively normal and its early stage, mild cognitive impairment (MCI), by just using structural MRI (sMRI). To attain this purpose, we proposed a multiclass classification method based on 3D T1-weight brain sMRI images from the ADNI database. Axial brain images were extracted from 3D MRI and fed into the convolutional neural network (CNN) for multiclass classification. Three separate models were tested: a CNN built from scratch, VGG-16, and ResNet-50. As a feature extractor, the VGG-16 and ResNet-50 convolutional bases trained on the ImageNet dataset were employed. To achieve classification, a new densely connected classifier was implemented on top of the convolutional bases.
Indexed as
Identifiers
What OpenQuestion holds
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.