Multimodal deep learning foundation models can learn the relationship between images and text. In the context of medical imaging, mapping images to language concepts reflects the clinical task of diagnostic image interpretation, however current general-purpose foundation models do not perform well in this context because their training corpus have limited medical text and images. To address this challenge and account for the range of cardiac physiology, we leverage 1,032,975 cardiac ultrasound videos and corresponding expert interpretations to develop EchoCLIP, a multimodal foundation model for echocardiography. EchoCLIP displays strong zero-shot (not explicitly trained) performance in cardiac function assessment (external validation left ventricular ejection fraction mean absolute error (MAE) of 7.1%) and identification of implanted intracardiac devices (areas under the curve (AUC) between 0.84 and 0.98 for pacemakers and artificial heart valves). We also developed a long-context variant (EchoCLIP-R) with a custom echocardiography report text tokenizer which can accurately identify unique patients across multiple videos (AUC of 0.86), identify clinical changes such as orthotopic heart transplants (AUC of 0.79) or cardiac surgery (AUC 0.77), and enable robust image-to-text search (mean cross-modal retrieval rank in the top 1% of candidate text reports). These emergent capabilities can be used for preliminary assessment and summarization of echocardiographic findings.

The article, "AI-Enabled ECG Improves Ability to Identify Heart Failure in Emergency Departments," was originally published on Practical Cardiology. An artificial intelligence (AI)-enabled electrocardiogram (ECG) could aid clinicians in emergency departments more accurately identify heart failure. Findings from the study indicate the AI-enhanced ECG could improve identification of left ventricular systolic dysfunction in patients presenting the emergency departments with acute dyspnea. "AI-enhanced ECGs are quicker and outperform current standard-of-care tests. Our results suggest that high-risk cardiac patients can be identified quicker in the emergency department and provides an opportunity to link them early to appropriate cardiovascular care," said lead investigator Demilade Adedinsewo, MD, MPH, chief fellow in the division of cardiovascular medicine at Mayo Clinic in Jacksonville, Florida, in a statement.

An artificial intelligence algorithm developed by Rochester, Minn.-based Mayo Clinic and cardiac monitoring startup Eko to analyze ECG data for evidence of reduced left ventricular ejection fraction has been designated a "breakthrough device" by the FDA. The algorithm reads ECG data collected by Eko's digital stethoscope to measure LVEF, which refers to the amount of blood pumped out of the heart's left ventricle and can indicate heart failure. The breakthrough device label, presented to technology with potential to address unmet clinical needs, will speed up regulatory review of the algorithm. Eko and Mayo Clinic's partnership to develop the AI algorithm began in late 2018. Since then, studies have shown that the algorithm-equipped stethoscope achieves significant accuracy in detecting low ejection fraction.