Deep-learning methods for enrichment of Alzheimer’s Disease clinical trials using MRI and PET
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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, Sweden
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Date: 03/04/2023