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Artificial Intelligence–Assisted Detection of Amyloid-Related Imaging Abnormalities: Promise and Pitfalls
Jeffrey R. Petrella, Ao Liu, Li Wang, Pudugramam Murali Doraiswamy. American Journal of Neuroradiology, 2025-07-30
Topic: Amyloid, tau and blood-test diagnosis
The advent of anti-amyloid therapies (AATs) for Alzheimer disease (AD) has elevated the importance of MRI surveillance for amyloid-related imaging abnormalities (ARIA) such as microhemorrhages and siderosis (ARIA-H) and edema (ARIA-E). We report a literature review and early quality-assurance experience with an FDA-cleared decision support artificial intelligence (AI) tool intended for detection of ARIA in MRI clinical workflows. The AI system improved the sensitivity for detection of subtle ARIA-E and ARIA-H lesions but at the cost of a reduction in specificity. We propose a tiered workflow combining protocol harmonization and expert interpretation with an AI overlay review. AI-assisted ARIA detection is a paradigm shift that offers great promise to enhance patient safety as disease-modifying therapies for AD gain broader clinical use; however, some pitfalls need to be considered.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Petrella, J. R., Liu, A., Wang, L., & Doraiswamy, P. M. (2025). Artificial Intelligence–Assisted Detection of Amyloid-Related Imaging Abnormalities: Promise and Pitfalls. American Journal of Neuroradiology. https://doi.org/10.3174/ajnr.a8946
Vancouver
Petrella JR, Liu A, Wang L, Doraiswamy PM. Artificial Intelligence–Assisted Detection of Amyloid-Related Imaging Abnormalities: Promise and Pitfalls. American Journal of Neuroradiology. 2025. doi:10.3174/ajnr.a8946. PMID: 40738658.
BibTeX
@article{petrella2025-40738658,
author = {Petrella, Jeffrey R. and Liu, Ao and Wang, Li and Doraiswamy, Pudugramam Murali},
title = {{Artificial Intelligence–Assisted Detection of Amyloid-Related Imaging Abnormalities: Promise and Pitfalls}},
journal = {American Journal of Neuroradiology},
year = {2025},
doi = {10.3174/ajnr.a8946},
pmid = {40738658},
url = {https://doi.org/10.3174/ajnr.a8946}
}
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Key findings
- The advent of anti-amyloid therapies (AATs) for Alzheimer disease (AD) has elevated the importance of MRI surveillance for amyloid-related imaging abnormalities (ARIA) such as microhemorrhages and siderosis (ARIA-H) and edema (ARIA-E).
- We report a literature review and early quality-assurance experience with an FDA-cleared decision support artificial intelligence (AI) tool intended for detection of ARIA in MRI clinical workflows.
- The AI system improved the sensitivity for detection of subtle ARIA-E and ARIA-H lesions but at the cost of a reduction in specificity.
- We propose a tiered workflow combining protocol harmonization and expert interpretation with an AI overlay review.
- AI-assisted ARIA detection is a paradigm shift that offers great promise to enhance patient safety as disease-modifying therapies for AD gain broader clinical use; however, some pitfalls need to be considered.
Why it matters
An FDA-cleared AI decision support tool improved sensitivity for detecting subtle ARIA-E and ARIA-H lesions on MRI but reduced specificity. The authors propose a tiered workflow combining protocol harmonization and expert interpretation with AI overlay review, noting both promise and pitfalls for patient safety as disease-modifying therapies gain broader use.
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Study details
- Evidence strengthLow
How this study scored 42/100
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 (American Journal of Neuroradiology) |
| Journal's citation record | 2 / 4 | American Journal of Neuroradiology: journal h-index 182 (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 | 4 / 5 | 2025 — within 3 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 6 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 2.0 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 2 / 6 | Cited 1.3× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 1 other studies in this library |
| Still being cited | 1 / 4 | Cited 6 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 4 / 4 | Last-listed author Pudugramam Murali Doraiswamy: h-index 105 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University of North Carolina at Chapel Hill: institution h-index 1,674 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40738658) |
| Free to read | 2 / 2 | Free to read |
| Total | 42 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.3174/ajnr.a8946
- PubMed: 40738658
- Original source →
- Free full text: American Journal of Neuroradiology →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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