Artificial Intelligence for a Better Understanding and Treatment of Arterial Disease
Seance of wednesday 30 april 2025 (Ce que le développement des technologies apporte à la sécurité et au soin des patient en chirurgie vasculaire)
DOI number : 10.26299/60rq-t349/2025.17.01
Abstract
Vascular surgery witnessed its first major technological breakthrough in the late 1950s with the introduction of vascular grafts. Since then, the field has progressively evolved, giving rise to modern endovascular therapies. Today, with an aging population, endovascular procedures have become the first-line approach for managing arterial disease. These treatments now encompass a wide range of innovative tools and techniques, including percutaneous transluminal angioplasty, stent placement, and intravascular lithotripsy—traditionally guided only by lesion length and severity of stenosis.
However, most of these devices and techniques are tested in vivo and ex vivo in animal models, with limited validation in humans.
A precise understanding of the histological composition of atherosclerotic plaques is essential to tailor endovascular treatment in a personalized, lesion-specific manner. The advent of micro-CT imaging now allows for detailed histological analysis of explanted arteries without destroying the samples. Artificial intelligence further enhances this capability by automatically identifying histological features on micro-CT images. Beyond that, artificial intelligence has the potential to transfer this expertise to preoperative imaging, enabling high-resolution, non-invasive assessments before intervention.
Ultimately, this work aims to highlight the transformative potential of artificial intelligence in improving arterial diagnostics and guiding individualized vascular treatment strategies.
However, most of these devices and techniques are tested in vivo and ex vivo in animal models, with limited validation in humans.
A precise understanding of the histological composition of atherosclerotic plaques is essential to tailor endovascular treatment in a personalized, lesion-specific manner. The advent of micro-CT imaging now allows for detailed histological analysis of explanted arteries without destroying the samples. Artificial intelligence further enhances this capability by automatically identifying histological features on micro-CT images. Beyond that, artificial intelligence has the potential to transfer this expertise to preoperative imaging, enabling high-resolution, non-invasive assessments before intervention.
Ultimately, this work aims to highlight the transformative potential of artificial intelligence in improving arterial diagnostics and guiding individualized vascular treatment strategies.