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Purpose: <p>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.</p> Methods: <p>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.</p> Results: <p>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 1&#xa0;mm.</p> Conclusion: <p>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.</p>

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

  • 期刊论文

  • 发表日期：

    2026年2月25日

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  • Deepak Raina,
  • Lidia Al-Zogbi,
  • Brian Teixeira,
  • Vivek Singh,
  • Ankur Kapoor,
  • Thorsten Fleiter,
  • Muyinatu A. Lediju Bell,
  • Vinciya Pandian,
  • Axel Krieger

摘要

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 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.

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