Evidence map›Paper›PMID 41739357›Full record

ArticleInternational journal of computer assisted radiology and surgery2026

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

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

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Article in International journal of computer assisted radiology and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Deepak RainaMalone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD 21218, USA.ORCID http://orcid.org/0000-0002-5863-6111
Lidia Al-ZogbiWhiting School of Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Brian TeixeiraMedical Imaging Technologies, Siemens Medical Solutions, Inc., Princeton, NJ 08540, USA.
Vivek SinghMedical Imaging Technologies, Siemens Medical Solutions, Inc., Princeton, NJ 08540, USA.
Ankur KapoorMedical Imaging Technologies, Siemens Medical Solutions, Inc., Princeton, NJ 08540, USA.
Thorsten FleiterR. Cowley Shock Trauma Center, Department of Diagnostic Radiology, School of Medicine, University of Maryland, Baltimore, MD 21201, USA.
Muyinatu A Lediju Bell *Johns Hopkins University, Baltimore, MD, USA.ORCID http://orcid.org/0000-0002-8394-4482
Vinciya Pandian *Ross and Carol Nese College of Nursing, The Pennsylvania State University, University Park, PA 16802, USA.ORCID http://orcid.org/0000-0002-2260-1080
Axel Krieger *Whiting School of Engineering and Malone Centre for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD 21218, USA. axel@jhu.edu.

Funding

AHRQ HHS 7R18HS029124-03National Science Foundation 2222716National Science Foundation 2431810Whiting School of Engineering, Johns Hopkins University 2144348Whiting School of Engineering, Johns Hopkins University Malone Postdoctoral Fellowship
6 · The paper itself

Abstract

purposeCentral 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.

methodsWe 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.

resultsThe 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.

conclusionTo 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.

Indexed as

Catheterization, Central VenousRoboticsRobotic Surgical ProceduresUltrasonography, InterventionalAutonomous RobotsHumansPhantoms, ImagingCentral venous catheterizationMotion-planningRobotic ultrasoundUltrasound-guided interventions

Identifiers

PMID41739357

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.