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.
Comparison of amyloid positivity and global cortical SUVR between black and white non-Hispanic participants in the GAP Bio-Hermes study
Summary: This poster shows our further investigation on the amyloid PET differences between Black and White participants in the GAP Bio-Hermes study and to better understand the relationship between Aβ+ status from visual read and quantitative SUVR.
Fully automatic deep-learning based segmentation of hippocampal subfields from standard resolution T1W MRI in Alzheimer’s disease
Here we train an AI method to segment the hippocampus into subfields (CA1-3 CA4+DG, and Subiculum) from standard resolution T1W MRI alone to assess utility as biomarkers compared to whole hippocampal volume, in the absence of a high-resolution T2 MRI.
Deep-learning methods for enrichment of Alzheimer’s Disease clinical trials using MRI and PET
Drug development trials aimed to halt Alzheimer’s disease (AD) progression favour recruitment of participants at early stages, preferably before symptomatic onset. In this investigation, we developed a deep-learning framework to differentiate participants with accelerated cognitive decline from those that remain cognitively stable within 24 months.