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Using the Alzheimer's Disease Neuroimaging Initiative dataset, the authors identified 2582 18F-florbetapir PET scans, which were separated into positive and negative cases by using a standardized uptake value ratio threshold of 1.1. They trained convolutional neural networks to predict standardized uptake value ratio and classify amyloid status. The best performance was seen for ResNet-50 by using regression before classification, 3 input PET slices, and pretraining, with a standardized uptake value ratio root-mean-squared error of 0.054, corresponding to 95.1% correct amyloid status prediction. The best trained network was more accurate than humans (96% versus a mean of 88%, respectively). They conclude that deep learning algorithms can estimate standardized uptake value ratio and use this to classify 18F-florbetapir PET scans and have promise to automate this laborious calculation.