AURA-CVC: Autonomous Ultrasound-guided Robotic Assistance for Central Venous Catheterization.
Authors
Affiliations (9)
Affiliations (9)
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD 21218, USA.
- Indian Institute of Technology Mandi, Himachal Pradesh, 175005, India.
- Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
- Vanderbilt University, Nashville, TN, 37235, USA.
- Medical Imaging Technologies, Siemens Medical Solutions, Inc., Princeton, NJ 08540, USA.
- R. Cowley Shock Trauma Center, Department of Diagnostic Radiology, School of Medicine, University of Maryland, Baltimore, MD 21201, USA.
- Johns Hopkins University, Baltimore, MD, USA.
- Ross and Carol Nese College of Nursing, The Pennsylvania State University, University Park, PA 16802, USA.
- Whiting School of Engineering and Malone Centre for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD 21218, USA. [email protected].
Abstract
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. We introduce a deep-learning model to identify clinically relevant anatomical landmarks from a depth image of the patient's neck, obtained using an 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. 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 mm, and autonomous needle insertion was performed with an error less than or close to 1Â mm. 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.