Zukotynski, KatherineGaudet, Vincent C.Uribe, Carlos F.Mathotaarachchi, SulanthaSmith, Kenneth C.Rosa-Neto, PedroBenard, FrancoisBlack, Sandra E.2023-11-032023-11-032021-01https://doi.org/10.2967/jnumed.119.231837http://hdl.handle.net/10012/20081COPYRIGHT © 2021 by the Society of Nuclear Medicine and Molecular Imaging.This article is the second part in our machine learning series. Part 1 provided a general overview of machine learning in nuclear medicine. Part 2 focuses on neural networks. We start with an example illustrating how neural networks work and a discussion of potential applications. Recognizing that there is a spectrum of applications, we focus on recent publications in the areas of image reconstruction, low-dose PET, disease detection, and models used for diagnosis and outcome prediction. Finally, since the way machine learning algo- rithms are reported in the literature is extremely variable, we conclude with a call to arms regarding the need for standardized reporting of design and outcome metrics and we propose a basic checklist our community might follow going forward.enmachine learningnuclear medicineneural networksArticle