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Artificial Intelligence Assistive Software Tool for Automated Detection and Quantification of Amyloid-Related Imaging Abnormalities
Sima DM, Phan TV, Van Eyndhoven S, Vercruyssen S, Magalhães R, Liseune A, Brys A, Frenyo P et al.. JAMA network open, 2024-02-05
Topic: Amyloid, tau and blood-test diagnosis
Importance Amyloid-related imaging abnormalities (ARIA) are brain magnetic resonance imaging (MRI) findings associated with the use of amyloid-β-directed monoclonal antibody therapies in Alzheimer disease (AD). ARIA monitoring is important to inform treatment dosing decisions and might be improved through assistive software. Objective To assess the clinical performance of an artificial intelligence (AI)-based software tool for assisting radiological interpretation of brain MRI scans in patients monitored for ARIA.
Key findings
- ARIA monitoring is important to inform treatment dosing decisions and might be improved through assistive software.
- Objective To assess the clinical performance of an artificial intelligence (AI)-based software tool for assisting radiological interpretation of brain MRI scans in patients monitored for ARIA.
- Design, setting, and participants This diagnostic study used a multiple-reader multiple-case design to evaluate the diagnostic performance of radiologists assisted by the software vs unassisted.
- The study enrolled 16 US Board of Radiology-certified radiologists to perform radiological reading with (assisted) and without the software (unassisted).
Why it matters
This diagnostic study assessed an artificial intelligence-based software tool for assisting radiological interpretation of brain MRI scans in patients monitored for amyloid-related imaging abnormalities (ARIA). It addresses monitoring of a treatment side effect, not treating, curing or preventing Alzheimer's disease itself.
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Study details
- Participants16
- Effect size52.8%; 11.6%; 0.5%
- Evidence strengthLow
How this study scored 53/100
Published in a leading specialty journal. Scored zero for study design and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 9 / 10 | Tier 2 — leading specialty journal (JAMA network open) |
| Journal's citation record | 2 / 4 | JAMA Network Open: journal h-index 235 (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 | 2024 — within 3 year(s) of the newest study here |
| Sample size | 1 / 4 | 16 participants |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| 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 | 4 / 7 | Cited 42 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 10.5 citations a year over 4 year(s) since 2024 |
| Impact for its field and year | 3 / 6 | Cited 3.4× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 5 other studies in this library |
| Still being cited | 2 / 4 | Cited 42 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Dirk Smeets: h-index 31 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Biogen (United States): institution h-index 495 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 38345816) |
| Free to read | 2 / 2 | Free to read |
| Total | 53 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1001/jamanetworkopen.2023.55800
- PubMed: 38345816
- Original source →
- Free full text: JAMA Network Open →
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. 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.
APA 7th
Sima, D. M., Phan, T. V., Van Eyndhoven, S., Vercruyssen, S., Magalhães, R., Liseune, A., et al. (2024). Artificial Intelligence Assistive Software Tool for Automated Detection and Quantification of Amyloid-Related Imaging Abnormalities. JAMA network open. https://doi.org/10.1001/jamanetworkopen.2023.55800
Vancouver
Sima DM, Phan TV, Van Eyndhoven S, Vercruyssen S, Magalhães R, Liseune A, et al. Artificial Intelligence Assistive Software Tool for Automated Detection and Quantification of Amyloid-Related Imaging Abnormalities. JAMA network open. 2024. doi:10.1001/jamanetworkopen.2023.55800. PMID: 38345816.
BibTeX
@article{sima2024-38345816,
author = {Sima, D. M. and Phan, T. V. and Van Eyndhoven, S. and Vercruyssen, S. and Magalhães, R. and Liseune, A. and Brys, A. and Frenyo, P. and others},
title = {{Artificial Intelligence Assistive Software Tool for Automated Detection and Quantification of Amyloid-Related Imaging Abnormalities}},
journal = {JAMA network open},
year = {2024},
doi = {10.1001/jamanetworkopen.2023.55800},
pmid = {38345816},
url = {https://doi.org/10.1001/jamanetworkopen.2023.55800}
}
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