SynthesisInternational journal of molecular sciences2021
Machine Learning and Novel Biomarkers for the Diagnosis of Alzheimer's Disease.
Synthesis in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 108 papers, 6 of them syntheses that pooled it.
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Who cites it
108 citing papers in PubMed, 6 syntheses or guidelines pooled it, 221 citations in OpenAlex.
- Leveraging artificial intelligence for analysis of the gut microbiome for dementia diagnosis: a scoping review and discussion.Frontiers in dementia · 2026Pooled it
- Integrating AI with Biosensors and Voltammetry for Neurotransmitter Detection and Quantification: A Systematic Review.Biosensors · 2025Pooled it
- Early Predictive Accuracy of Machine Learning for Hemorrhagic Transformation in Acute Ischemic Stroke: Systematic Review and Meta-Analysis.Journal of medical Internet research · 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
- Pooled it
- The application of artificial intelligence in diagnosis of Alzheimer's disease: a bibliometric analysis.Frontiers in neurology · 2024Pooled it
- The global research of artificial intelligence on Alzheimer disease: A 25-year bibliometric analysis.Medicine · 2026Article
- Radiomics-enhancedEuropean radiology experimental · 2026Article
- The mechanisms and strategies for Alzheimer's disease prevention: An update.Acta pharmaceutica Sinica. B · 2026Review
- Machine Learning-Assisted Plasma Metabolomics Identifies a Five-Metabolite Panel for Colorectal Cancer Detection.ACS omega · 2026Article
- Next generation preventive neurology: how artificial intelligence and machine learning are reshaping Alzheimer's disease research.Behavioral and brain functions : BBF · 2026Review
- Review
- Review
- Plasma Proteomic Signatures for Alzheimer's Disease: Comparable Accuracy to ATN Biomarkers and Cross-Platform Validation.Annals of clinical and translational neurology · 2026Article
- Alzheimer's disease detection using a quantum deep neural network with Haralick feature extraction and simulated annealing optimization.PeerJ. Computer science · 2026Article
- Precise diagnosis of Alzheimer's disease based on sex-specific gray matter characteristics.Frontiers in neuroscience · 2026Article
- Advancing Drug Discovery with AI: Machine and Deep Learning Strategies for Target Identification and Precision Nanomedicine.International journal of nanomedicine · 2026Review
- Enhancing Early Diagnosis: Multimodal AI Approaches for Neurodegenerative Diseases.Journal of biotechnology and biomedicine · 2026Article
- Sex-dependent grey matter atrophy in Alzheimer's disease progression.Brain communications · 2026Article
- Inflammatory and neuroendocrine biomarkers associated with mental health symptoms across body mass index categories: a cross-sectional machine learning analysis.Frontiers in immunology · 2026Article
48 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlzheimer's disease (AD) is a complex and severe neurodegenerative disease that still lacks effective methods of diagnosis. The current diagnostic methods of AD rely on cognitive tests, imaging techniques and cerebrospinal fluid (CSF) levels of amyloid-β1-42 (Aβ42), total tau protein and hyperphosphorylated tau (p-tau). However, the available methods are expensive and relatively invasive. Artificial intelligence techniques like machine learning tools have being increasingly used in precision diagnosis.
methodsWe conducted a meta-analysis to investigate the machine learning and novel biomarkers for the diagnosis of AD.
methodsWe searched PubMed, the Cochrane Central Register of Controlled Trials, and the Cochrane Database of Systematic Reviews for reviews and trials that investigated the machine learning and novel biomarkers in diagnosis of AD.
resultsIn additional to Aβ and tau-related biomarkers, biomarkers according to other mechanisms of AD pathology have been investigated. Neuronal injury biomarker includes neurofiliament light (NFL). Biomarkers about synaptic dysfunction and/or loss includes neurogranin, BACE1, synaptotagmin, SNAP-25, GAP-43, synaptophysin. Biomarkers about neuroinflammation includes sTREM2, and YKL-40. Besides, d-glutamate is one of coagonists at the NMDARs. Several machine learning algorithms including support vector machine, logistic regression, random forest, and naïve Bayes) to build an optimal predictive model to distinguish patients with AD from healthy controls.
conclusionsOur results revealed machine learning with novel biomarkers and multiple variables may increase the sensitivity and specificity in diagnosis of AD. Rapid and cost-effective HPLC for biomarkers and machine learning algorithms may assist physicians in diagnosing AD in outpatient clinics.
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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.