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Progress in Machine Learning-Assisted Biosensors for Alzheimer’s Disease
Yan Feng, Changdong Chen. Biosensors, 2026-03-13
Topic: Amyloid, tau and blood-test diagnosis
Alzheimer's disease (AD) is the most common cause of dementia, affecting 55 million people worldwide. Its characteristics include the accumulation of senile plaques and neurofibrillary tangles. This disease is associated with changes in the concentration of AD biomarkers, such as microRNAs, amyloid peptides, Tau protein, and neurofilament light chains. Due to the fact that neuropathological processes begin decades before the onset of cognitive symptoms, accurate detection of AD biomarkers is crucial for its early diagnosis. The combination of analytical techniques and machine learning methods plays a crucial role in medical innovation. Recently, efforts have been made to develop machine learning-assisted biosensors for AD diagnosis. This article provides an overview of the progress in machine learning-assisted sensing of AD biomarkers in bodily fluids. It mainly includes three parts: machine learning algorithms, machine learning-assisted electrochemical and optical biosensors, and challenges and future perspectives. We believe that this work will contribute to the development of innovative analytical devices based on artificial intelligence for monitoring and managing neurodegenerative diseases.
Key findings
- This disease is associated with changes in the concentration of AD biomarkers, such as microRNAs, amyloid peptides, Tau protein, and neurofilament light chains.
Why it matters
This review covers progress in machine learning-assisted biosensors for detecting Alzheimer's disease biomarkers such as microRNAs, amyloid peptides, Tau protein, and neurofilament light chains in bodily fluids. It is about early detection technology, not about treating, curing, or preventing the disease.
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Study details
- Evidence strengthLow
How this study scored 38/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Biosensors) |
| Journal's citation record | 1 / 4 | Biosensors: journal h-index 124 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 5 / 5 | 2026 — within 1 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 2 / 7 | Cited 5 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 2.5 citations a year over 2 year(s) since 2026 |
| Impact for its field and year | 4 / 6 | Cited 8.8× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 1 / 4 | Cited 5 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Changdong Chen: h-index 21 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Pingdingshan University: institution h-index 87 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 41892053) |
| Free to read | 2 / 2 | Free to read |
| Total | 38 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Feng, Y., & Chen, C. (2026). Progress in Machine Learning-Assisted Biosensors for Alzheimer’s Disease. Biosensors. https://doi.org/10.3390/bios16030161
Vancouver
Feng Y, Chen C. Progress in Machine Learning-Assisted Biosensors for Alzheimer’s Disease. Biosensors. 2026. doi:10.3390/bios16030161. PMID: 41892053.
BibTeX
@article{feng2026-41892053,
author = {Feng, Yan and Chen, Changdong},
title = {{Progress in Machine Learning-Assisted Biosensors for Alzheimer’s Disease}},
journal = {Biosensors},
year = {2026},
doi = {10.3390/bios16030161},
pmid = {41892053},
url = {https://doi.org/10.3390/bios16030161}
}
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