Advancing Embodied Intelligence in Robotic-Assisted Endovascular Procedures: A Systematic Review of AI Solutions
Journal:
arXiv
Published Date:
Apr 21, 2025
Abstract
Endovascular procedures have revolutionized the treatment of vascular
diseases thanks to minimally invasive solutions that significantly reduce
patient recovery time and enhance clinical outcomes. However, the precision and
dexterity required during these procedures poses considerable challenges for
interventionists. Robotic systems have emerged offering transformative
solutions, addressing issues such as operator fatigue, radiation exposure, and
the inherent limitations of human precision. The integration of Embodied
Intelligence (EI) into these systems signifies a paradigm shift, enabling
robots to navigate complex vascular networks and adapt to dynamic physiological
conditions. Data-driven approaches, advanced computer vision, medical image
analysis, and machine learning techniques, are at the forefront of this
evolution. These methods augment procedural intelligence by facilitating
real-time vessel segmentation, device tracking, and anatomical landmark
detection. Reinforcement learning and imitation learning further refine
navigation strategies and replicate experts' techniques. This review
systematically examines the integration of EI principles into robotic
technologies, in relation to endovascular procedures. We discuss recent
advancements in intelligent perception and data-driven control, and their
practical applications in robot-assisted endovascular procedures. By critically
evaluating current limitations and emerging opportunities, this review
establishes a framework for future developments, emphasizing the potential for
greater autonomy and improved clinical outcomes. Emerging trends and specific
areas of research, such as federated learning for medical data sharing,
explainable AI for clinical decision support, and advanced human-robot
collaboration paradigms, are also explored, offering insights into the future
direction of this rapidly evolving field.
