Agreement Between Amyloid Status Prediction from Visual Read, SUVR Thresholding, and AI-Based Interpretation of Amyloid PET
Our poster explores how visual read, SUVR thresholding, and a deep learning AI model perform in predicting amyloid status from PET imaging. We discuss how AI amyloid status predictions may help identify subtle, spatial uptake patterns that global SUVR can miss and how combining these approaches could better support readers.
This has direct relevance for clinical trials, where accurate and consistent amyloid assessment is critical for participant selection, monitoring disease progression, and evaluating treatment effects.
IXICO’s ongoing research into integrating AI-derived predictions with SUVR seeks to provide standardized, quantitative support to visual reads, improving confidence in borderline cases and streamlining clinical trial workflows