Artificial Intelligence Monitoring of ARIA‐H in Real‐World Patients Receiving Anti‐Amyloid Therapy
The findings demonstrate that the proposed AI system can be used to identify low-risk monitoring timepoints for ARIA-H. This suggests potential for reducing radiology workload and serving as a screener for radiology evaluation, thereby enhancing the efficiency, cost-effectiveness, consistency, and accessibility of ARIA-H monitoring. Future work will incorporate longitudinal ARIA-H training data to improve the PPV and specificity.
Authors
- Ilya M. Nasrallah (ORCID: https://orcid.org/0000-0003-2346-7562)
- David A. Wolk (ORCID: https://orcid.org/0000-0002-3554-3481)
- John A. Detre (ORCID: https://orcid.org/0000-0002-8115-6343)
- Long Xie
- Sandhitsu R. Das (ORCID: https://orcid.org/0000-0003-0530-2887)
- Christopher Brown (ORCID: https://orcid.org/0000-0002-8627-5579)
- Paul A. Yushkevich
- Manuel Taso
- Eli Gibson
Institutions
- Siemens (United States) (US)
- Siemens Healthcare (United States) (US)
- University of Pennsylvania (US)
Publication Details
- Journal
- Alzheimer s & Dementia
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1002/alz70861_108066
- Primary Topic
- Intracerebral and Subarachnoid Hemorrhage Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00