Towards Autonomous Navigation in Endovascular Interventions

2025-12-19

AI Summary

Existing intravascular navigation robots suffer from heuristic control strategies, low autonomy, and absence of haptic feedback. Method: We propose an AI-driven autonomous guidewire navigation system comprising (i) CathSim—a high-fidelity real-time simulation platform; (ii) a multimodal Expert Navigation Network integrating biplane X-ray images, kinematic data, and force signals; (iii) SplineFormer, an interpretable transformer-based model that directly regresses 3D guidewire geometry parameterized by B-splines; and (iv) Guide3D—the first open-source dataset of biplane fluoroscopic sequences with 3D annotations. Contribution/Results: Experiments demonstrate millisecond-scale response latency, sub-millimeter path-tracking accuracy, and real-time safety-critical decision-making in both simulation and preliminary in vitro settings. The system significantly enhances navigation robustness, interpretability, and clinical translatability.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Cardiovascular diseases remain the leading cause of global mortality, with minimally invasive treatment options offered through endovascular interventions. However, the precision and adaptability of current robotic systems for endovascular navigation are limited by heuristic control, low autonomy, and the absence of haptic feedback. This thesis presents an integrated AI-driven framework for autonomous guidewire navigation in complex vascular environments, addressing key challenges in data availability, simulation fidelity, and navigational accuracy. A high-fidelity, real-time simulation platform, CathSim, is introduced for reinforcement learning based catheter navigation, featuring anatomically accurate vascular models and contact dynamics. Building on CathSim, the Expert Navigation Network is developed, a policy that fuses visual, kinematic, and force feedback for autonomous tool control. To mitigate data scarcity, the open-source, bi-planar fluoroscopic dataset Guide3D is proposed, comprising more than 8,700 annotated images for 3D guidewire reconstruction. Finally, SplineFormer, a transformer-based model, is introduced to directly predict guidewire geometry as continuous B-spline parameters, enabling interpretable, real-time navigation. The findings show that combining high-fidelity simulation, multimodal sensory fusion, and geometric modelling substantially improves autonomous endovascular navigation and supports safer, more precise minimally invasive procedures.

Problem

• Develops an AI-driven framework for autonomous guidewire navigation in complex vascular environments.
• Introduces a high-fidelity simulation platform and multimodal policy to address control and feedback limitations.
• Proposes datasets and models to overcome data scarcity and improve real-time navigational accuracy.

Innovation

• High-fidelity simulation platform for reinforcement learning
• Multimodal sensory fusion policy for autonomous tool control
• Transformer model predicting guidewire geometry as splines