Use of artificial intelligence for detecting left ventricular dysfunction and predicting incident heart failure risk.

Abstract

Effective medications are available for the prevention of heart failure (HF). While their use is indicated in patients with risk factors, engagement and adherence among 'at risk' individuals is challenging, as it is with atherosclerotic heart disease prevention. The detection of patients with subclinical cardiac dysfunction could provide a subgroup at heightened risk, warranting more intensive disease management programmes. The process of screening the aging population is a huge task that could be facilitated using artificial intelligence (AI) to identify clinical risk, select 'at risk' individuals by using AI to enhance the value of electocardiography, and facilitate the non-expert acquisition and interpretation of echocardiography. This review, informed by a search of the recent literature, explored how such an AI-informed pathway could permit HF screening to occur in the community-maximizing access and minimizing cost.