ReviewSensors (Basel, Switzerland)2023
Revolutionizing the Early Detection of Alzheimer's Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning.
Review in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 3 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
54 citing papers in PubMed, 3 syntheses or guidelines pooled it, 134 citations in OpenAlex.
- 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
- Stage-Wise IoT Solutions for Alzheimer's Disease: A Systematic Review of Detection, Monitoring, and Assistive Technologies.Sensors (Basel, Switzerland) · 2025Pooled it
- Machine Learning Driven by Magnetic Resonance Imaging for the Classification of Alzheimer Disease Progression: Systematic Review and Meta-Analysis.JMIR aging · 2024Pooled it
- Machine Learning in Dementia Research: Trends, Biomarkers, Prediction Goals, and Data Modalities.Brain and behavior · 2026Article
- Machine Learning and Multimodal Biomarker Discovery in Alzheimer's Disease.Brain sciences · 2026Review
- Clinical pathways matter for multimodal deep learning in early Alzheimer's disease detection.Scientific reports · 2026Article
- From Molecular Networks to Medicines: Targeting Complexity in Alzheimer's Disease (AD) Therapy.Molecular neurobiology · 2026Review
- Detecting Beta-Amyloid Plaque via Low Rank Based Orthogonal Projection and Spatial-Spectrum Detector Using High-Resolution Quantitative Susceptibility Mapping for Preclinical Studies.IEEE transactions on bio-medical engineering · 2026Article
- NeuroFusion-ViT: A Hybrid CNN-EVA Transformer Model with Cross-Attention Fusion for MRI-Based Alzheimer's Stage Classification.Diagnostics (Basel, Switzerland) · 2026Article
- Intelligent decision-making systems for early detection of alzheimer's disease using wearable technologies and deep learning.Scientific reports · 2026Article
- Graphomotor performance as a quantitative marker of Alzheimer's disease: a tablet-based case-control ROC study.Frontiers in neuroscience · 2026Article
- Computational Approaches to Neurological Disorder Diagnosis: An In-Depth Review of Current Methods and Future ProspectsCurrent medical imaging · 2026Review
- PredatorNet: a clinically trustworthy deep learning framework for multi-class Alzheimer's disease classification using MRI.Frontiers in aging neuroscience · 2026Article
- Revolutionizing Neurodegenerative Disease Management: The Synergy of AI and Pharmacy.Current aging science · 2026Review
- A Hybrid Explainable AI Framework (HXAI) for Accurate and Interpretable Diagnosis of Alzheimer's Disease.Diagnostics (Basel, Switzerland) · 2025Article
- Clinical Decision Support Systems and Artificial Intelligence in ADHD Assessment and Rehabilitation: Opportunities and Challenges for Technology-Assisted Care.Healthcare (Basel, Switzerland) · 2025Article
- Exploring Exosome-Based Approaches for Early Diagnosis and Treatment of Neurodegenerative Diseases.Molecular neurobiology · 2025Review
- Automated Alzheimer's disease detection using active learning model with reinforcement learning and scope loss function.Npj mental health research · 2025Article
- Artificial Intelligence-Assisted Nanosensors for Clinical Diagnostics: Current Advances and Future Prospects.Biosensors · 2025Review
- Optimizing the Color Shapes Task for Ambulatory Assessment and Drift Diffusion Modeling: A Factorial Experiment.JMIR formative research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Alzheimer's disease (AD) is now classified as a silent pandemic due to concerning current statistics and future predictions. Despite this, no effective treatment or accurate diagnosis currently exists. The negative impacts of invasive techniques and the failure of clinical trials have prompted a shift in research towards non-invasive treatments. In light of this, there is a growing need for early detection of AD through non-invasive approaches. The abundance of data generated by non-invasive techniques such as blood component monitoring, imaging, wearable sensors, and bio-sensors not only offers a platform for more accurate and reliable bio-marker developments but also significantly reduces patient pain, psychological impact, risk of complications, and cost. Nevertheless, there are challenges concerning the computational analysis of the large quantities of data generated, which can provide crucial information for the early diagnosis of AD. Hence, the integration of artificial intelligence and deep learning is critical to addressing these challenges. This work attempts to examine some of the facts and the current situation of these approaches to AD diagnosis by leveraging the potential of these tools and utilizing the vast amount of non-invasive data in order to revolutionize the early detection of AD according to the principles of a new non-invasive medicine era.
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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.