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Automated quantification of cerebral microbleeds for ARIA-H monitoring in Aging and Alzheimer’s Disease: A multicenter deep learning validation
Low ZXB, Rowsthorn E, Nazem-Zadeh M, Francis M, Robb C, Whiriskey R, Howcroft M, Brodtmann A et al.. medRxiv, 2026-05-26
Topic: Plasma and spinal-fluid biomarkers
We trained a self-configuring nnU-Net model for CMB segmentation in a hetero-geneous multicenter sample ( n =264), including 1.5T and 3T field strengths, SWI and T2*-GRE sequences, and community and clinical cohorts. Model performance was evaluated using 5-fold cross-validation with a focus on object-level detection metrics. Real-world performance was evaluated on scans from an unseen dataset of people with cerebrovascular disease ( n =20). The model achieved 0.82 cluster Dice, 0.88 precision, and 0.77 sensitivity on hold-out test data. Notably, the model demonstrated a low false-positive rate, averaging 0.58 false positives (FPs) per scan, an improvement on existing publicly available models. The model achieved high performance in dataset of those with Alzheimer’s disease and mild cognitive impairment (0.89 cluster Dice, 0.94 sensitivity), supporting its utility in clinical settings where ARIA-H monitoring is critical. In external validation, the model maintained high robustness with 0.79 sensitivity and 0.95 FPs per scan. By leveraging a heterogenous training strategy and a self-adapting architecture, we demonstrate that deep learning can achieve high-precision CMB detection that is robust to domain shifts. The low FP rate suggests this publicly available pipeline is suitable for automated screening and lesion counting in heterogenous large-scale clinical trials, reducing the burden of manual quantification.
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APA 7th
Low, Z. X. B., Rowsthorn, E., Nazem-Zadeh, M., Francis, M., Robb, C., Whiriskey, R., et al. (2026). Automated quantification of cerebral microbleeds for ARIA-H monitoring in Aging and Alzheimer’s Disease: A multicenter deep learning validation. medRxiv. https://doi.org/10.64898/2026.05.19.26353364
Vancouver
Low ZXB, Rowsthorn E, Nazem-Zadeh M, Francis M, Robb C, Whiriskey R, et al. Automated quantification of cerebral microbleeds for ARIA-H monitoring in Aging and Alzheimer’s Disease: A multicenter deep learning validation. medRxiv. 2026. doi:10.64898/2026.05.19.26353364.
BibTeX
@article{low2026-23d6501e,
author = {Low, Z. X. B. and Rowsthorn, E. and Nazem-Zadeh, M. and Francis, M. and Robb, C. and Whiriskey, R. and Howcroft, M. and Brodtmann, A. and others},
title = {{Automated quantification of cerebral microbleeds for ARIA-H monitoring in Aging and Alzheimer’s Disease: A multicenter deep learning validation}},
journal = {medRxiv},
year = {2026},
doi = {10.64898/2026.05.19.26353364},
url = {https://doi.org/10.64898/2026.05.19.26353364}
}
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Key findings
- Notably, the model demonstrated a low false-positive rate, averaging 0.58 false positives (FPs) per scan, an improvement on existing publicly available models.
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Study details
- Participants264
- Evidence strengthLow
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 29 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for study design, effect reported and randomised / blinded because the record does not state it. We have no data on 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 18 of the 62 points we could judge it on, which is 29 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (medRxiv) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| 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 | 4 / 10 | Preprint — not yet peer-reviewed. Hosted on a preprint server. |
| How recent | 5 / 5 | 2026 — within 1 year(s) of the newest study here |
| Sample size | 2 / 4 | 264 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 | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 18 / 62 | Scored on 12 of 20 parts. We have no data for the rest, so they are left out: 18 of the 62 points we could judge it on is 29 out of 100. |
| Total | 29 / 100 | Capped to 16 because this is not a peer-reviewed article. |
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