Machine Learning in Nuclear Medicine: Part 2—Neural Networks and Clinical Aspects
Katherine Zukotynski, Vincent Gaudet, Carlos Uribe, Sulantha Mathotaarachchi, K.C. Smith, Pedro Rosa‐Neto, François Bénard, Sandra E. Black
Abstract
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 algorithms 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.