Poterucha, Timothy

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.

Abstract Echocardiography is the most widely used cardiac imaging modality, yet artificial intelligence-enabled interpretation remains limited by the inability of existing models to integrate visual assessment, quantitative measurement, and clinical reasoning within a unified framework. Here we present EchoAtlas, the first autoregressive vision-language model developed for echocardiographic interpretation. Trained on over 12.9 million question-answer pairs derived from approximately 2 million echocardiogram videos, EchoAtlas achieves 0.966 accuracy on multiple-choice questions in our internal test set and establishes a new state-of-the-art on the public MIMIC-EchoQA benchmark (0.699 vs. 0.508 previously). EchoAtlas also provides accurate quantitative measurements, segment-level regional wall motion assessment, longitudinal comparison, and diagnostic reasoning across diverse question formats — capabilities not previously demonstrated in this domain. These results highlight the potential of autoregressive vision-language models as a foundation for interactive echocardiographic interpretation, representing an early step toward scalable, auditable artificial intelligence systems in cardiology practice.

Abstract Valvular heart disease (VHD) remains significantly underdiagnosed and undertreated. This review examines an artificial intelligence (AI)-enhanced ‘spoke–hub–node’ care model designed to improve the early detection, risk stratification, and treatment of VHD. In this model, AI tools—such as automated ECG interpretation, digital stethoscopes, and point-of-care ultrasound—facilitate decentralized screening and referral for cardiac imaging at the community level. During the transition from outpatient settings to tertiary care centres, AI-integrated echocardiography, cardiac tomography, and magnetic resonance imaging facilitate advanced diagnostic evaluation and inform procedural planning. We review emerging innovations that can enhance this model of care delivery—including unsupervised machine learning to uncover novel VHD phenotypes, generative AI for automated reporting, the use of digital twins to simulate interventions, and the integration of multiple AI agents to support heart team meetings. These advances are followed by the emerging use of AI in robotic transoesophageal and intracardiac echocardiography, as well as in fusion fluoroscopy imaging, to guide valve interventions. While outlining the challenges inherent in this rapidly evolving field, the review’s central contribution is its vision to connect the continuum—from AI-enabled community screening to personalized, image-guided therapies at tertiary care centres—offering a scalable and equitable model for VHD care.