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Cited by 257Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatmentsNeuroinflammation and Neurodegeneration Mechanisms

Machine Learning and Novel Biomarkers for the Diagnosis of Alzheimer’s Disease

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Chang, Chun-Hung, Lin, Chieh-Hsin, Lane, Hsien-Yuan·2021·International Journal of Molecular Sciences
DOI 10.3390/ijms22052761PMC PMC7963160
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Abstract

Background: Alzheimer’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. Methods: We conducted a meta-analysis to investigate the machine learning and novel biomarkers for the diagnosis of AD. Methods: We 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. Results: In 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. Conclusions: Our 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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  • Machine learning and artificial intelligence applied to multimodal datasets are poised to transform precision neurology by enabling predictive stratification, individualized risk forecasting, and identification of therapeutic targets embedded within disease networks.

    “Machine learning and artificial intelligence, applied to such datasets, are poised to transform precision neurology by enabling predictive stratification, individualized risk forecasting, and the identification of therapeutic targets embedded within disease networks rather than isolated pathways [178].”

    From Toward a Unified Framework in Molecular Neurobiology of Alzheimer’s Disease: Revisiting the Pathophysiological Hypotheses (2026) · cites this paper as [178]

    The Introduction and Outlook and Future Direction (pages unavailable) describe machine learning with biomarkers as promising for predicting Alzheimer’s disease and cognitive outcomes. Section 3.4 also discusses AI-assisted discovery of candidate NMDAR antagonists. The paper does not support the broader claim that these approaches will transform precision neurology through individualized risk forecasting or identification of therapeutic targets within disease networks rather than isolated pathways. (LLM, conf=0.88)

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Cited by

Sorted by citations

Authors
Chang, C.-H., Lin, C.-H., & Lane, H.-Y.
Affiliations
  • China Medical University (TW)
  • Chang Gung University (TW)
  • Asia University (TW)
Corresponding author
  • Lin, Chieh-Hsin
  • Lane, Hsien-Yuan
ORCID
Venue
International Journal of Molecular Sciences
Publisher
MDPI AG
Published
2021-03-09
Volume
22
Issue
5
Pages
2761
ISSN
1422-0067 (ISSN-L)
DOI
10.3390/ijms22052761
PMC
PMC7963160
OpenAlex
W3134680770
Cited by
257
References
11
Citations by year
2026: 49 · 2025: 61 · 2024: 67 · 2023: 49 · 2022: 24 · 2021: 7
FWCI
6.53
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Molecular Sciences (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • China Medical University Hospital
  • Chang Gung Medical Foundation
  • Ministry of Science and Technology, Taiwan (MOST108-2628-B182A-002, MOST109-2628-B182A-002, MOST 107?, MOST109-2314-B039-039-MY3, MOST107-2628-B182A-002)
  • National Health Research Institutes (NHRI-EX109-10816NC, NHRI-EX110- 10816NC)
  • China Medical University
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
  • Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
Keywords
Alzheimer's disease, machine learning, diagnostic biomarkers, amyloid-beta 42, tau protein, neurofilament light chain, neurogranin, BACE1, synaptophysin, neuroinflammation, sTREM2, YKL-40, D-glutamic acid, NMDA receptor, support vector machine, random forest, logistic regression, naive Bayes, early diagnosis
MeSH terms
Machine Learning, Aged, Alzheimer Disease, Chromatography, High Pressure Liquid, Diagnosis, Computer-Assisted, Female, Humans, Middle Aged, Biomarkers

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