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Related Concept Videos

Cardiac Catheterization IV: Nursing Management01:26

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Nursing responsibilities before cardiac catheterization include:Assess for allergies and establish baseline health status.Before cardiac catheterization, assess the patient for allergies to contrast dye. Perform a comprehensive baseline assessment, including vital signs, heart and breath sounds, and a neurovascular assessment of the extremities, noting distal pulses, skin color, and temperature. Instruct the patient to fast for 8-12 hours before the procedure. Evaluate baseline laboratory...
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Related Experiment Video

Updated: Feb 26, 2026

The Supraclavicular Fossa Ultrasound View for Central Venous Catheter Placement and Catheter Change Over Guidewire
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AURA-CVC: Autonomous Ultrasound-guided Robotic Assistance for Central Venous Catheterization.

Deepak Raina1,2, Lidia Al-Zogbi3,4, Brian Teixeira5

  • 1Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD 21218, USA.

International Journal of Computer Assisted Radiology and Surgery
|February 25, 2026
PubMed
Summary

This study presents an autonomous robotic system for ultrasound-guided central venous catheterization (CVC), achieving 100% success in phantom trials. The system integrates planning, scanning, and needle insertion for improved vascular access.

Keywords:
Central venous catheterizationMotion-planningRobotic ultrasoundUltrasound-guided interventions

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Area of Science:

  • Medical Robotics
  • Ultrasound Imaging
  • Vascular Access

Background:

  • Central venous catheterization (CVC) is vital but technically demanding, requiring precise ultrasound guidance.
  • Operator dependency and anatomical variations pose challenges, increasing risks of complications.
  • Robotic systems show promise for autonomous CVC, but integrated solutions are limited.

Purpose of the Study:

  • To develop and validate an end-to-end robotic pipeline for autonomous ultrasound-guided CVC.
  • To address challenges in vessel visualization, localization, and needle insertion.
  • To improve the safety and efficacy of central venous catheterization.

Main Methods:

  • A deep-learning model identifies anatomical landmarks from RGB-D depth images for autonomous scanning region definition.
  • A robot motion planning framework performs vessel scanning, segmentation, reconstruction, and localization.
  • A needle guidance module facilitates autonomous insertion under ultrasound guidance with operator feedback.

Main Results:

  • The pipeline achieved 10 successful needle placements on the first attempt in 10 simulated scenarios.
  • Vessels were reconstructed with a mean error of 2.15 mm.
  • Autonomous needle insertion accuracy was less than or close to 1 mm.

Conclusions:

  • This work demonstrates the first robotic CVC system with integrated planning, scanning, and insertion on a high-fidelity phantom.
  • The system shows significant potential for clinical translation in improving central venous catheterization.
  • Autonomous robotic guidance offers a promising approach to enhance safety and precision in vascular access procedures.