A groundbreaking study reveals that an innovative artificial intelligence (AI)-powered imaging technique offers comparable outcomes to conventional ultrasound-guided methods when used for minimally invasive stent placement in patients with significant coronary artery blockages. Researchers presented these findings at the American College of Cardiology’s Annual Scientific Session.

This trial, referred to as FLAVOUR II, focused on patients experiencing narrowing in three major coronary arteries supplying blood to the heart. The new AI-based method, known as AngioFFR, calculates blood flow from a single x-ray view of the coronary arteries and presents a non-invasive alternative to traditional techniques. According to Jian’an Wang, the senior author of the study, patients guided by this novel approach underwent fewer procedures while maintaining similar health outcomes compared to those treated using the more established ultrasound-based imaging.

FLAVOUR II involved 1,872 participants across 22 medical centers in China, showcasing its potential applicability among a broad demographic. Eligible patients had at least a 50% narrowing in three main arteries but no significant issues in the left main coronary artery. Most also faced additional health challenges such as hypertension, diabetes, or high cholesterol levels. Participants were randomly assigned to either receive stent placement guided by AngioFFR or intravascular ultrasound (IVUS). After one year, both groups showed comparable rates of death, heart attacks, or the need for repeat procedures, indicating the viability of the AI-driven method.

The integration of advanced technology into medical practices continues to redefine patient care. This study exemplifies how innovation can enhance treatment options while potentially reducing procedure counts. Although limitations exist—such as the absence of complex lesion cases and a lack of global patient diversity—the results underscore the promise of AI in cardiology. By continuing long-term follow-ups, researchers aim to uncover any emerging differences in effectiveness between these two methodologies, further solidifying the role of AI in modern medicine.