We discuss our latest work to evaluate a fully automated image analysis pipeline to process vesicular monoamine transporters type 2 (VMAT2) [18F]AV-133 tracer positron emission tomography (PET) images, by comparison with a methodology requiring manual intervention.
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.
As members of the Ataxia Global Initiative (AGI) MR Biomarkers Study Group that authored the paper Kirsi Kinnunen and Niccolo Fuin hope that these guidelines on harmonizing MRI data acquisition will be helpful for ataxia study sponsors.
We have developed a fully automated framework that uses deep learning for caudate segmentation (IXIQ. Ai) and generalised BSI (gBSI) for longitudinal measurements. Here, we validate the new method by comparing its volumetric scores with those of the standard manual pipeline (Man+BSI). Man+BSI produced larger caudate volumes than IXIQ.
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.