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

TL;DR

This paper presents a fully autonomous robotic system for ultrasound-guided central venous catheterization, integrating deep learning, planning, and needle insertion, validated on a high-fidelity phantom with promising accuracy and success rates.

Contribution

It introduces the first integrated robotic CVC pipeline with deep learning-based landmark detection, vessel segmentation, and autonomous needle insertion validated on a realistic phantom.

Findings

Achieved 10/10 successful needle placements on first attempt

Reconstructed vessels with mean error of 2.15 mm

Performed autonomous needle insertion with error less than 1 mm

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 \textit{mm}, and autonomous needle insertion was performed with an error less than or close to 1 \textit{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.