The DOI matches its Crossref record; 156 of the 157 references it cites were found in a registry or an index.
Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer’s disease dementia: a systematic review
Open accessAbstract
Background: An increase in lifespan in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer’s disease being the most prevalent. Advances in medical imaging and computational power enable new methods for the early detection of neurocognitive disorders with the goal of preventing or reducing cognitive decline. Computer-aided image analysis and early detection of changes in cognition is a promising approach for patients with mild cognitive impairment, sometimes a prodromal stage of Alzheimer’s disease dementia. Methods: We conducted a systematic review following PRISMA guidelines of studies where machine learning was applied to neuroimaging data in order to predict whether patients with mild cognitive impairment might develop Alzheimer’s disease dementia or remain stable. After removing duplicates, we screened 452 studies and selected 116 for qualitative analysis. Results: Most studies used magnetic resonance image (MRI) and positron emission tomography (PET) data but also magnetoencephalography. The datasets were mainly extracted from the Alzheimer’s disease neuroimaging initiative (ADNI) database with some exceptions. Regarding the algorithms used, the most common was support vector machine with a mean accuracy of 75.4%, but convolutional neural networks achieved a higher mean accuracy of 78.5%. Studies combining MRI and PET achieved overall better classification accuracy than studies that only used one neuroimaging technique. In general, the more complex models such as those based on deep learning, combined with multimodal and multidimensional data (neuroimaging, clinical, cognitive, genetic, and behavioral) achieved the best performance. Conclusions: Although the performance of the different methods still has room for improvement, the results are promising and this methodology has a great potential as a support tool for clinicians and healthcare professionals.
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![Fig. 1 PRISMA workflow for study selection adapted from Moher et al. [18]. WOS Web Of Science, AD Alzheimer’s disease, MCI mild cognitive impairment](/paper-figures/e8b853700f8e4721a54c100c3b465053/machine-learning-methods-for-predicting-progression-from-mild-cognitive-figure-1.jpg)
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“By integrating neuroimaging, genetic, and clinical data, researchers have developed models that can identify individuals at high risk of developing Alzheimer’s disease [15–18].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [15]
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Synthetic EMG signal generation for intelligent healthcare systems: A review toward society 5.0
Cano-Salas, N., Rodríguez‐Reséndiz, J., & Avilés, M. (2026) · Entrepreneurship, Engineering and Management Conference · doi:10.5937/pim26388c
VISTA: Velocity-Informed Sequential Transition Analysis
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Khoshdel, S., Talemi, N. A., Omidi, M., Thrift, J. R., Adams, S. A., Bhanu, A., Afghah, F., Hummel, K., & Rahemi, Z. (2026) · doi:10.2196/preprints.114326
Metadata
- Authors
- Grueso, S., & Viejo-Sobera, R.
- Affiliations
- Universitat Oberta de Catalunya (ES)
- Corresponding author
- Grueso, Sergio
- Venue
- Alzheimer's Research & Therapy
- Published
- 2021-09-28
- Volume
- 13
- Issue
- 1
- Pages
- 162-162
- ISSN
- 1758-9193 (ISSN-L)
- PMC
- PMC8480074
- OpenAlex
- W3202902530
- Cited by
- 303
- References
- 157
- Citations by year
- 2026: 60 · 2025: 84 · 2024: 77 · 2023: 48 · 2022: 33 · 2021: 1
- FWCI
- 7.10
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Alzheimer s Research & Therapy (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- O2 - Repositori Institucional (Universitat Oberta de Catalunya) (submittedVersion, cc-by)
- Funders
- Universitat Oberta de Catalunya
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Keywords
- mild cognitive impairment, Alzheimer's dementia, progression prediction, machine learning, neuroimaging, magnetic resonance imaging, PET, magnetoencephalography, multimodal data, convolutional neural networks, support vector machine, deep learning, early detection, ADNI
- MeSH terms
- Machine Learning, Alzheimer Disease, Brain, Humans, Magnetic Resonance Imaging, Disease Progression, Neuroimaging, Cognitive Dysfunction
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