Scientists at Yale University are using artificial intelligence (AI) to interpret electrocardiograms (ECGs) to provide an automated screening method for left ventricular (LV) systolic dysfunction.

The condition reduces the pumping capacity of the heart and is associated with frequent hospitalisations and a twofold risk of premature death. LV dysfunction is preventable with timely detection and initiation of medications. However, identifying the disease before symptom onset has not been feasible.

To address this, Rohan Khera and his team from the university’s Cardiovascular Data Science (CarDS) Lab have developed an AI-based ECG interpretation designed for global use.

The study appeared last month in the journal Circulation.

A cardiologist cannot identify patients with LV dysfunction without an ECG or MRI scan. Diagnosing LV systolic dysfunction – a weakness in the major chamber of the heart – requires cardiac imaging. Broad screening for the disorder is limited by technology and available expertise. However, an ECG is the most accessible cardiovascular diagnostic test obtained in clinical practice globally.

In their design, the team included nearly 400,000 ECGs paired with data on heart dysfunction from imaging tests. The algorithm was tested across different formats with data from several US clinics and hospitals, as well as in a large community cohort in Brazil.

“We demonstrated that a simple photo or scanned image of a 12-lead ECG, the most well-recognised and easily obtained cardiac test, can provide key insights on cardiac structure and function disorders,” Khera said. “This opens up the possibility to finally bring a screening tool for such disorders that affect up to one in 20 adults globally. Their diagnosis is frequently delayed as advanced testing is either unavailable or only reserved for those with symptomatic disease. Now we can identify these patients with a simple web-based or smartphone application.”

Veer Sangha, the first author of the study and a member of the CarDS Lab, added: “Our approach creates a super-reader of ECG images, identifying signatures of LV systolic dysfunction, which the human eye cannot accurately decipher.”

And Khera said: “Our AI tool allows for early diagnosis and treatment and also identifies those at future risk of developing LV dysfunction. The findings represent our ongoing effort to make application of AI-driven advanced ECG inference accessible.”

The study was supported in part by a grant from the National Heart, Lung & Blood Institute of the National Institutes of Health.