Uriel, Nir

Abstract Aims Heart failure (HF), a major global health challenge, affects millions worldwide and poses substantial healthcare and economic burdens. It is estimated that a large proportion of those with early systolic dysfunction remain asymptomatic at a stage when guideline-directed medical therapies have been shown to prevent disease progression. To develop an artificial intelligence (AI) model capable of predicting abnormal left ventricular ejection fraction (EF) directly from static, non-gated, non-contrast chest computed tomography (CT) scans as a form of opportunistic screening, Methods and results Using a multi-institutional dataset of 34 058 paired non- contrast CT images and echocardiogram reports from two academic centres, we trained our model of classification for predicting left-ventricle ejection fraction (LVEF) categories: abnormal EF (EF < 50%) vs. normal on 25 948 studies. We validated the model on 8110 paired chest CT and echocardiogram results from a separate institution. The model achieved an area under the receiver operating characteristic (AUROC) curve of 0.786 on the hold-out test set and 0.762 on external validation to detect an abnormal EF (<50%). Beyond strong predictive performance, the AI model surpassed expert radiologists in both accuracy and efficiency and provided interpretable visualizations highlighting imaging features linked to reduced LVEF. Conclusion In this study, we developed and validated an AI model capable of predicting abnormal LVEF directly from static, non-gated, non-contrast chest CT scans, a novel application for an imaging modality typically used for unrelated indications as a form of opportunistic screening. This technology holds significant promise for early detection of systolic HF, reducing the diagnostic gap, and improving outcomes in asymptomatic HF patients.

Right ventricular pressure-volume (PV) analysis characterizes ventricular systolic and diastolic properties independent of loading conditions like volume status and afterload. While long-considered the gold-standard method for quantifying myocardial chamber performance, it was traditionally only performed in highly specialized research settings. With recent advances in catheter technology and more sophisticated approaches to analyze PV data, it is now more commonly used in a variety of clinical and research settings. Herein, we review the basic techniques for PV loop measurement, analysis, and interpretation with the aim of providing readers with a deeper understanding of the strengths and limitations of PV analysis. In the second half of the review, we detail key scenarios in which right ventricular PV analysis has influenced our understanding of clinically relevant topics and where the technique can be applied to resolve additional areas of uncertainty. All told, PV analysis has an important role in advancing our understanding of right ventricular physiology and its contribution to cardiovascular function in health and disease.