Medical-AI trial: intravascular-ultrasound (IVUS)-based AI to improve outcomes after coronary stenting (INNOVATE-PCI) (NCT05807841)

In angina and heart attack, where the coronary arteries feeding the heart narrow, catheter treatment that widens the narrowing and places a stent (PCI, percutaneous coronary intervention) is widely performed.

During it, beyond angiography (a silhouette of the vessel), intravascular ultrasound (IVUS), which views the inside of the vessel in cross-section from the catheter tip, allows precise assessment of plaque character, degree of narrowing, and how well the stent is apposed. But reading IVUS images takes expertise and is sometimes underused.

This study validates, in real practice, the power of ML models that analyze these angiography and IVUS images, as a prospective, multicenter observational study. Per the registry summary, it validates the diagnostic performance and clinical impact of models developed by ML based on coronary angiography and IVUS in real-world practice.

The primary outcomes are culprit-related target-vessel failure (TVF), related to the treated lesion, and nonculprit-related TVF, related to untreated lesions. TVF is a composite of adverse cardiac outcomes tied to the vessel (such as restenosis, repeat treatment, or heart attack). The size is 3,000, and it is recruiting.

IVUS is precise but its reading requires expertise, and turning that information into better outcomes takes experience. If AI can aid IVUS analysis and foresee not only the treated site but the future risk from lesions not yet treated, it could help optimize the stent and decide the next step. Prospective, multicenter validation in real practice aims to capture the actual clinical impact that constrained trials can miss.

That said, this study validates diagnostic performance and clinical impact; it does not establish treatment decisions or effectiveness.