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<p>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.</p><p></p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月4日

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  • Lingsen You,
  • Jiaxin Yao,
  • Yaoqing Qiu,
  • Yu Wang,
  • Yunlu Sun,
  • Rongjun Zhang,
  • Li Shen,
  • Junbo Ge

摘要

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.