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.
Conclusions: SUVR levels were associated with future cognitive decline in MCI but not in controls, while the CNN embeddings were able to identify both groups. Deep-learning algorithms offer a reliable framework to predict cognitive decline in MCI and control participants.
Presented at ADPD 2023 in Gothenburg, SwedenDownload