Digital quantification of vascular β-amyloid and perivascular plaques in cerebral amyloid angiopathy.

Introduction: This study aimed to 1) develop a quantitative approach to automate detection of cortical CAA-positive vessels separate from β-amyloid plaques, 2) validate digital pathology classifier for CAA against manual scoring, and 3) characterize the spatial distribution of perivascular β-amyloid plaques in relation to CAA-positive vessels. Methods: Digital pathology classifiers were trained on the HALO AI platform to distinguish cortical CAA-positive vessels from β-amyloid plaques in the calcarine cortex. Classifiers were applied to digitized histopathologic slides (n = 100), previously rated by expert neuropathologists. Slide level digital quantitative measures for CAA (CAA classified area and vascular β-amyloid percent positivity) and for β-amyloid plaques (β-amyloid plaque classified area) were compared with manual CAA scores and Alzheimer's disease neuropathologic change (ADNC). Perivascular plaques, defined as β-amyloid plaques within a 0- to 150-micron concentric distance from the center of CAA-positive vessels, were identified. Results: Participants had a mean age of 91.1 (SD = 5.6) years at death, with 76% being women. Digital pathology quantification for CAA classified area and vascular β-amyloid percent positivity showed strong correlations with semi-quantitative manual CAA scores (r Conclusion: Digital pathology classifiers reliably distinguish cortical CAA vessels from β-amyloid plaques and can be utilized to study plaque proximity to CAA-positive vessels severity, supporting the use of digital pathology to advance understanding of CAA pathogenesis.