ARTIFICIAL INTELLIGENCE-ASSISTED FUNCTIONAL ASSESSMENT OF BORDERLINE CORONARY STENOSES IN MOBILE ANGIOGRAPHY: INTEGRATION WITH PERSONALIZED CARDIAC REHABILITATION

Abstract

Background: Borderline coronary stenoses (40–70%) represent a major clinical challenge in interventional cardiology, particularly in mobile angiography settings where invasive functional assessment tools such as Fractional Flow Reserve (FFR) and instant wave-free ratio (iFR) are largely unavailable. Artificial intelligence (AI)-based approaches offer a potential solution for real-time functional triage, yet their applicability in resource-limited mobile environments and integration with downstream cardiac rehabilitation remain unexplored.