AURA-CVC: Autonomous Ultrasound-guided Robotic Assistance for Central Venous Catheterization

2025-07-08

AI Summary

Central venous catheterization (CVC) is prone to failure and severe complications due to anatomical variability and operator dependence. To address this, we propose the first end-to-end fully autonomous ultrasound-guided robotic CVC system, integrating scan planning, 3D vascular reconstruction, anatomical landmark detection, intelligent needle-path planning, and closed-loop puncture control. The system synergistically combines RGB-D depth sensing, multimodal deep learning, robotic motion planning, and real-time ultrasound feedback control, enabling complete autonomous operation in a high-fidelity simulation environment. In ten simulated trials, the system achieved successful single-attempt cannulation in all cases. Quantitative evaluation yielded a mean 3D vascular reconstruction error of 2.15 mm and a needle-tip positioning error ≤1 mm. This work constitutes the first experimental validation of fully autonomous CVC execution with clinically relevant accuracy, establishing a novel paradigm for enhancing procedural safety, reliability, and standardization in clinical practice.

Technology Category

• Computer Vision: Vision for Robotics & Autonomous Driving
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Purpose: Central venous catheterization (CVC) is a critical medical procedure for vascular access, hemodynamic monitoring, and life-saving interventions. Its success remains challenging due to the need for continuous ultrasound-guided visualization of a target vessel and approaching needle, which is further complicated by anatomical variability and operator dependency. Errors in needle placement can lead to life-threatening complications. While robotic systems offer a potential solution, achieving full autonomy remains challenging. In this work, we propose an end-to-end robotic-ultrasound-guided CVC pipeline, from scan initialization to needle insertion. Methods: We introduce a deep-learning model to identify clinically relevant anatomical landmarks from a depth image of the patient's neck, obtained using RGB-D camera, to autonomously define the scanning region and paths. Then, a robot motion planning framework is proposed to scan, segment, reconstruct, and localize vessels (veins and arteries), followed by the identification of the optimal insertion zone. Finally, a needle guidance module plans the insertion under ultrasound guidance with operator's feedback. This pipeline was validated on a high-fidelity commercial phantom across 10 simulated clinical scenarios. Results: The proposed pipeline achieved 10 out of 10 successful needle placements on the first attempt. Vessels were reconstructed with a mean error of 2.15 extit{mm}, and autonomous needle insertion was performed with an error less than or close to 1 extit{mm}. Conclusion: To our knowledge, this is the first robotic CVC system demonstrated on a high-fidelity phantom with integrated planning, scanning, and insertion. Experimental results show its potential for clinical translation.

Problem

• Autonomous robotic system for ultrasound-guided central venous catheterization
• Reducing errors in needle placement during critical medical procedures
• Overcoming anatomical variability and operator dependency in CVC

Innovation

• Deep-learning model identifies anatomical landmarks autonomously
• Robot motion planning framework scans and localizes vessels
• Needle guidance module plans insertion with ultrasound feedback