Exploring Racial and Ethnic Differences in Utilization of Medications for Obesity Management in a Nationally Representative Survey.

Background: The burden of obesity falls disproportionately on some racial and ethnic minority groups.

Objective: To assess for racial and ethnic differences in the utilization of obesity-management medications among clinically eligible individuals.

Design: Medical Expenditure Panel Survey (2011-2016, 2018 and 2020) data and a cross-sectional study design was used to assess for racial and ethnic differences in obesity-management medication utilization.

Qualitative American Heart Association plot of late gadolinium enhancement with mortality and ventricular arrhythmia prediction using artificial intelligence.

Background: The prognostic value of late gadolinium enhancement (LGE) in cardiac magnetic resonance (CMR) imaging is well-established. However, the direct relationship between image pixels and outcomes remains poorly understood. We hypothesised that leveraging artificial intelligence (AI) to analyse qualitative LGE images based on American Heart Association (AHA) guidelines could elucidate this relationship.

Automated cardiovascular MR myocardial scar quantification with unsupervised domain adaptation.

Quantification of myocardial scar from late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) images can be facilitated by automated artificial intelligence (AI)-based analysis. However, AI models are susceptible to domain shifts in which the model performance is degraded when applied to data with different characteristics than the original training data. In this study, CycleGAN models were trained to translate local hospital data to the appearance of a public LGE CMR dataset.