ArcheD, a residual neural network for prediction of cerebrospinal fluid amyloid‐beta from amyloid PET images

Detection and measurement of amyloid‐beta (Aβ) in the brain is a key factor for early identification and diagnosis of Alzheimer's disease (AD). We aimed to develop a deep learning model to predict Aβ cerebrospinal fluid (CSF) concentration directly from amyloid PET images, independent of tracers, brain reference regions or preselected regions of interest. We used 1870 Aβ PET images and CSF measurements to train and validate a convolutional neural network (“ArcheD”). We evaluated the ArcheD performance in relation to episodic memory and the standardized uptake value ratio (SUVR) of cortical Aβ. We also compared the brain region's relevance for the model's CSF prediction within clinical‐based and biological‐based classifications. ArcheD‐predicted Aβ CSF values correlated with measured Aβ CSF values (r = 0.92; q