From intravascular imaging to adaptive vascular care: intelligent photonics and digital twins in panvascular disease

Abstract

Panvascular diseases (PVDs) stand as the leading cause of global mortality, necessitating a paradigm shift from local anatomical repair to the systemic restoration of vascular homeostasis. While intravascular optical imaging has revolutionized diagnosis, it remains a passive observation tool, restricted by "physical bottlenecks" in resolution and "cognitive bottlenecks" in interpretation. To address these challenges, we frame our analysis around "Suitcordance", a concept aiming to capture the dynamic state of matching between interventional devices and the vascular microenvironment. In this review, we use Suitcordance as a working analytical framework to represent such a clinically-targeted, integrated perspective, and to organize the evidence on intravascular optical imaging and its integration with artificial intelligence. First, we summarize recent advances in intravascular imaging modalities, including micro-OCT, hybrid systems, and emerging detection technologies. Second, we review how AI-based image analysis and image-derived digital twin models are being applied to interpret these data and to support procedural decision-making. On this basis, we discuss how such tools may contribute to a more individualized assessment of device-vessel matching in panvascular disease.