AI-Assisted Detection of Amyloid-related Imaging Abnormalities (ARIA): Promise and Pitfalls.
Authors
Affiliations (1)
Affiliations (1)
- From the Departments of Radiology (J.R.P), Neurology (A.J.L.) and Psychiatry (P.M.D., L.A.W), Duke University School of Medicine, Durham NC, USA; Duke-UNC Alzheimer's Disease Research Center (J.R.P, A.J.L., P.M.D.), Durham/Chapel Hill, NC, USA.
Abstract
The advent of anti-amyloid therapies (AATs) for Alzheimer's disease (AD) has elevated the importance of MRI surveillance for amyloidrelated 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 assistive AI tool intended for detection of ARIA in MRI clinical workflows. The AI system improved 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 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.ABBREVIATIONS: AAT＝ anti-amyloid therapy; ARIA＝ amyloid-related imaging abnormalities, ARIA-H = amyloid-related imaging abnormality-hemorrhage, ARIA-E = amyloid-related imaging abnormality-edema.