PT  - JOURNAL ARTICLE
AU  - Katherine Zukotynski
AU  - Vincent Gaudet
AU  - Carlos F. Uribe
AU  - Sulantha Mathotaarachchi
AU  - Kenneth C. Smith
AU  - Pedro Rosa-Neto
AU  - François Bénard
AU  - Sandra E. Black
TI  - Machine Learning in Nuclear Medicine: Part 2—Neural Networks and Clinical Aspects
AID  - 10.2967/jnumed.119.231837
DP  - 2021 Jan 01
TA  - Journal of Nuclear Medicine
PG  - 22--29
VI  - 62
IP  - 1
4099  - http://jnm.snmjournals.org/content/62/1/22.short
4100  - http://jnm.snmjournals.org/content/62/1/22.full
SO  - J Nucl Med2021 Jan 01; 62
AB  - 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.