Despite the great media attention for artificial intelligence (AI), for many health care
professionals the term and the functioning of AI remain a “black box,” leading to exaggerated …

This article, the first in a 2-part series, provides an introduction to machine learning (ML) in a
nuclear medicine context. This part addresses the history of ML and describes common …

Background Cardiovascular magnetic resonance (CMR) is increasingly used for risk
stratification in aortic stenosis (AS). However, the relative prognostic power of CMR markers …

Objectives Optimizing a machine learning (ML) pipeline for radiomics analysis involves
numerous choices in data set composition, preprocessing, and model selection. Objective …

Recent advances in artificial intelligence are expected to cause significant paradigm shift in
all digital data-driven aspects of information gain, processing and decision making in both …

Abstract Background and Aim Severe acute pancreatitis (SAP) in patients progresses rapidly
and can cause multiple organ failures associated with high mortality. We aimed to train a …

Artificial intelligence involves a wide range of smart techniques that are applicable to
medical services including nuclear medicine. Recent advances in computer power …

Artificial intelligence (AI) and deep learning (DL) remains a hot topic in medicine. DL is a
subcategory of machine learning that takes advantage of multiple layers of interconnected …

Purpose To demonstrate that random forest models trained on a large national sample can
accurately predict relevant outcomes and may ultimately contribute to future clinical decision …

Myocardial perfusion imaging (MPI) plays an important role in patients with suspected and
documented coronary artery disease (CAD). Machine Learning (ML) algorithms have been …