ReviewDiagnostics (Basel, Switzerland)2024
Early Alzheimer's Disease Detection: A Review of Machine Learning Techniques for Forecasting Transition from Mild Cognitive Impairment.
Review in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis 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
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Molecular Biomarkers for Early Detection of Alzheimer's Disease and the Complementary Role of Engineered Nanomaterials: A Systematic Review.International journal of molecular sciences · 2025Pooled it
- Low-burden AI approach for cross-national early identification of cognitive impairment using real-world questionnaire response behaviours.Nature communications · 2026Article
- Clinical pathways matter for multimodal deep learning in early Alzheimer's disease detection.Scientific reports · 2026Article
- Review
- A causal deep learning approach to identifying metabolic signatures of cognitive and functional decline in alzheimer's disease.Scientific reports · 2026Article
- Multimodal non-invasive approaches for early Alzheimer's disease detection: a review of neuroelectrophysiological and neuroimaging techniques.Frontiers in psychiatry · 2026Review
- HyperTransFusion: a hypernetwork transformer with black winged kite optimization for multimodal early Alzheimer's disease diagnosis.Frontiers in digital health · 2026Article
- Metaheuristic-driven dual-layer model for classifying Alzheimer's disease stages.Frontiers in computational neuroscience · 2026Article
- Early detection of Alzheimer's disease via multimodal MRI and machine learning.Frontiers in aging neuroscience · 2026Article
- Predicting progression of Alzheimer's disease using blood-based multi-omics data.Bioinformatics advances · 2026Article
- Early diagnosis of Alzheimer's disease using machine learning and blood biomarkers.BMC medical informatics and decision making · 2025Article
- An Explainable Web-Based Diagnostic System for Alzheimer's Disease Using XRAI and Deep Learning on Brain MRI.Diagnostics (Basel, Switzerland) · 2025Article
- Machine Learning-Powered Smart Healthcare Systems in the Era of Big Data: Applications, Diagnostic Insights, Challenges, and Ethical Implications.Diagnostics (Basel, Switzerland) · 2025Review
- Time-Frequency Domain Analysis of Quantitative Electroencephalography as a Biomarker for Dementia.Diagnostics (Basel, Switzerland) · 2025Review
- Ensemble Learning-Based Alzheimer's Disease Classification Using Electroencephalogram Signals and Clock Drawing Test Images.Sensors (Basel, Switzerland) · 2025Article
- Characterizing bidirectional transitions in mild cognitive impairment and post-reversion based on longitudinal neuroimaging and cognitive assessments.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Clinical Utility of an Alzheimer's Disease Blood Test Among Cognitively Impaired Patients: Results from the Quality Improvement PrecivityAD2 (QUIP II) Clinician Survey Study.Diagnostics (Basel, Switzerland) · 2025Article
- An efficient method for early Alzheimer's disease detection based on MRI images using deep convolutional neural networks.Frontiers in artificial intelligence · 2025Article
- Explainable bidirectional encoder representations from image transformers for Alzheimer's disease prediction.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Alzheimer's disease is a weakening neurodegenerative condition with profound cognitive implications, making early and accurate detection crucial for effective treatment. In recent years, machine learning, particularly deep learning, has shown significant promise in detecting mild cognitive impairment to Alzheimer's disease conversion. This review synthesizes research on machine learning approaches for predicting conversion from mild cognitive impairment to Alzheimer's disease dementia using magnetic resonance imaging, positron emission tomography, and other biomarkers. Various techniques used in literature such as machine learning, deep learning, and transfer learning were examined in this study. Additionally, data modalities and feature extraction methods analyzed by different researchers are discussed. This review provides a comprehensive overview of the current state of research in Alzheimer's disease detection and highlights future research directions.
Indexed as
Identifiers
What Socratic 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 Socratic graph.