ArticleNeurobiology of aging2021
Transfer learning for predicting conversion from mild cognitive impairment to dementia of Alzheimer's type based on a three-dimensional convolutional neural network.
Article in Neurobiology of aging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 4 of them syntheses that pooled it.
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
34 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- The future of Alzheimer's disease risk prediction: a systematic review.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Pooled it
- Predictive modelling of brain disorders with magnetic resonance imaging: A systematic review of modelling practices, transparency, and interpretability in the use of convolutional neural networks.Human brain mapping · 2023Pooled it
- Interpretable machine learning for dementia: A systematic review.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Pooled 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
- HIBMatch: Hypergraph Information Bottleneck for Semi-Supervised Alzheimer's Progression.IEEE journal of biomedical and health informatics · 2026Article
- Predicting categorical and continuous Alzheimer's disease outcomes from a single MRI scan.Nature aging · 2026Article
- Predicting Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using a Vision Transformer and Hippocampal MRI Slices.Bioengineering (Basel, Switzerland) · 2026Article
- Nanomedicine-Driven Modulation of the Gut-Brain Axis: Innovative Approaches to Managing Chronic Inflammation in Alzheimer's and Parkinson's Disease.International journal of molecular sciences · 2025Review
- Hybrid multi-modality multi-task learning for forecasting progression trajectories in subjective cognitive decline.Neural networks : the official journal of the International Neural Network Society · 2025Article
- Transfer Learning and Neural Network-Based Approach on Structural MRI Data for Prediction and Classification of Alzheimer's Disease.Diagnostics (Basel, Switzerland) · 2025Article
- A hybrid transformer-based approach for early detection of Alzheimer's disease using MRI images.BioImpacts : BI · 2025Article
- Unraveling the Mysteries of Alzheimer's Disease Using Artificial Intelligence.Reviews on recent clinical trials · 2025Review
- Structure focused neurodegeneration convolutional neural network for modelling and classification of Alzheimer's disease.Scientific reports · 2024Article
- Predicting Progression From Mild Cognitive Impairment to Alzheimer's Dementia With Adversarial Attacks.IEEE journal of biomedical and health informatics · 2024Article
- A Review of Datasets, Optimization Strategies, and Learning Algorithms for Analyzing Alzheimer's Dementia Detection.Neuropsychiatric disease and treatment · 2024Review
- Differential diagnosis of frontotemporal dementia subtypes with explainable deep learning on structural MRI.Frontiers in neuroscience · 2024Article
- Translating phenotypic prediction models from big to small anatomical MRI data using meta-matching.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Predicting Four-Year's Alzheimer's Disease Onset Using Longitudinal Neurocognitive Tests and MRI Data Using Explainable Deep Convolutional Neural Networks.Journal of Alzheimer's disease : JAD · 2024Article
- A bilateral filtering-based image enhancement for Alzheimer disease classification using CNN.PloS one · 2024Article
- Harnessing Transfer Learning for Dementia Prediction: Leveraging Sex-Different Mild Cognitive Impairment Prognosis.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
Dementia of Alzheimer's type (DAT) is associated with devastating and irreversible cognitive decline. Predicting which patients with mild cognitive impairment (MCI) will progress to DAT is an ongoing challenge in the field. We developed a deep learning model to predict conversion from MCI to DAT. Structural magnetic resonance imaging scans were used as input to a 3-dimensional convolutional neural network. The 3-dimensional convolutional neural network was trained using transfer learning; in the source task, normal control and DAT scans were used to pretrain the model. This pretrained model was then retrained on the target task of classifying which MCI patients converted to DAT. Our model resulted in 82.4% classification accuracy at the target task, outperforming current models in the field. Next, we visualized brain regions that significantly contribute to the prediction of MCI conversion using an occlusion map approach. Contributory regions included the pons, amygdala, and hippocampus. Finally, we showed that the model's prediction value is significantly correlated with rates of change in clinical assessment scores, indicating that the model is able to predict an individual patient's future cognitive decline. This information, in conjunction with the identified anatomical features, will aid in building a personalized therapeutic strategy for individuals with MCI.
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.