Home › Research › Treatments
Deep learning-based detection of cerebral microbleeds on 2D T2*-weighted GRE MRI: toward ARIA-H risk assessment in Alzheimer's treatment
Yang SO, Ahn J, Jung YH, Jang H, Na DL, Kim H, Kim JP, Seo SW et al.. Frontiers in aging neuroscience, 2026-03-23
Topic: Amyloid, tau and blood-test diagnosis
Background Amyloid-related imaging abnormalities with hemorrhage (ARIA-H) are a key safety concern in anti-amyloid therapies for Alzheimer's disease, as they are radiologically indistinguishable from cerebral microbleeds (CMBs). Accurate detection of CMBs is therefore essential for both treatment eligibility assessment and post-treatment safety monitoring. However, manual identification on 2D T2*-weighted gradient-recalled echo (GRE) MRI is labor-intensive and subject to variability. Objective To develop and validate an artificial intelligence (AI)-based model for automated CMB detection using only 2D T2*-weighted GRE MRI, which is widely used in clinical settings. Methods We implemented a YOLOv11-based deep learning model, preceded by a novel multi-channel preprocessing pipeline that enhances CMB visibility. The model was trained and tested using a dataset of 758 participants, with expert consensus used as the reference standard. Results Using the optimized basic preprocessing with super-resolution (BP + SR) pipeline, the model achieved a lesion-level sensitivity of 0.694, precision of 0.705, and F1-score of 0.699. In patient-level analysis for detecting elevated CMB burden (≥4), the system demonstrated sensitivity of 0.933 and specificity of 0.935, supporting reliable stratification of CMB severity. Regional analysis showed sensitivity of 0.625 for lobar CMBs and 0.627 for deep structures. Conclusion This study demonstrates the feasibility of robust CMB detection using only 2D T2*-weighted GRE MRI. Based on current performance, we position this system as a decision-support tool for GRE-based CMB screening, in which lesion-level detections may be aggregated to inform patient-level CMB burden relevant to ARIA-H risk stratification, while final ARIA grading and clinical decisions require expert neuroradiological confirmation.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Yang, S. O., Ahn, J., Jung, Y. H., Jang, H., Na, D. L., Kim, H., et al. (2026). Deep learning-based detection of cerebral microbleeds on 2D T2*-weighted GRE MRI: toward ARIA-H risk assessment in Alzheimer's treatment. Frontiers in aging neuroscience. https://doi.org/10.3389/fnagi.2026.1729422
Vancouver
Yang SO, Ahn J, Jung YH, Jang H, Na DL, Kim H, et al. Deep learning-based detection of cerebral microbleeds on 2D T2*-weighted GRE MRI: toward ARIA-H risk assessment in Alzheimer's treatment. Frontiers in aging neuroscience. 2026. doi:10.3389/fnagi.2026.1729422. PMID: 41948063.
BibTeX
@article{yang2026-41948063,
author = {Yang, S. O. and Ahn, J. and Jung, Y. H. and Jang, H. and Na, D. L. and Kim, H. and Kim, J. P. and Seo, S. W. and others},
title = {{Deep learning-based detection of cerebral microbleeds on 2D T2*-weighted GRE MRI: toward ARIA-H risk assessment in Alzheimer's treatment}},
journal = {Frontiers in aging neuroscience},
year = {2026},
doi = {10.3389/fnagi.2026.1729422},
pmid = {41948063},
url = {https://doi.org/10.3389/fnagi.2026.1729422}
}
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
Key findings
- Results Using the optimized basic preprocessing with super-resolution (BP + SR) pipeline, the model achieved a lesion-level sensitivity of 0.694, precision of 0.705, and F1-score of 0.699.
- In patient-level analysis for detecting elevated CMB burden (≥4), the system demonstrated sensitivity of 0.933 and specificity of 0.935, supporting reliable stratification of CMB severity.
New to this subject? Start with our plain-English guide: Treatments →
Study details
- Participants758
- Evidence strengthLow
How this study scored 55/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 32 of the 58 points we could judge it on, which is 55 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Frontiers in aging neuroscience) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| 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 | 2 / 4 | 758 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 41948063) |
| Free to read | 2 / 2 | Free to read |
| Points | 32 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 32 of the 58 points we could judge it on is 55 out of 100. |
| Total | 55 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
Free copy unknown: no free-copy check has an answer for this record yet. That does not mean it is paywalled.
Related
Studies in the library that share something with this one. The line above each title says what.
Related studies in Treatments
Also tagged “cerebral-microbleeds”
2 studies in the library carry the topic tag “cerebral-microbleeds”; these are this study's neighbours in a fixed, alphabetical-by-hash tour of them, not the closest in content. All 2 tagged studies.