Evidence map›Paper›PMID 26405563›Full record

ArticleIEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and Automation2015

Semi-autonomous Simulated Brain Tumor Ablation with RavenII Surgical Robot using Behavior Tree.

Danying Hu, Yuanzheng Gong, Blake Hannaford, Eric J Seibel

Abstract read
In one paragraph

Article in IEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and Automation, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Review
  5. Comparing the accuracy of the da Vinci Xi and da Vinci Si for image guidance and automation.The international journal of medical robotics + computer assisted surgery : MRCAS · 2020
    Article
  6. Review
  7. Review
  8. Optical Coherence Tomography-Guided Robotic Ophthalmic Microsurgery via Reinforcement Learning from Demonstration.IEEE transactions on robotics : a publication of the IEEE Robotics and Automation Society · 2020
    Article
  9. Article
  10. Review
  11. Semi-autonomous image-guided brain tumour resection using an integrated robotic system: A bench-top study.The international journal of medical robotics + computer assisted surgery : MRCAS · 2018
    Article
  12. Run-to-Run Optimization Control Within Exact Inverse Framework for Scan Tracking.Journal of dynamic systems, measurement, and control · 2017
    Article
  13. Toward real-time tumor margin identification in image-guided robotic brain tumor resection.Proceedings of SPIE--the International Society for Optical Engineering · 2017
    Article
  14. Article
  15. Toward real-time endoscopically-guided robotic navigation based on a 3D virtual surgical field model.Proceedings of SPIE--the International Society for Optical Engineering
    Article
  16. Path Planning for Semi-automated Simulated Robotic Neurosurgery.Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems
    Article
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

4 authors.

Danying HuBiorobotics Laboratory, Department of Electrical Engineering, University of Washington, Seattle, WA 98195, USA.
Yuanzheng GongHuman Photonics Laboratory, Department of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA.
Blake HannafordBiorobotics Laboratory, Department of Electrical Engineering, University of Washington, Seattle, WA 98195, USA.
Eric J SeibelHuman Photonics Laboratory, Department of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA.

Funding

NRI: Advanced Biophotonics for Image-guided Robotic SurgeryR01EB016457 · NIBIB · UNIVERSITY OF WASHINGTON · PI SEIBEL, ERIC J · 2012 to 2015
$1.2M
NIBIB NIH HHS R01 EB016457
6 · The paper itself

Abstract

Medical robots have been widely used to assist surgeons to carry out dexterous surgical tasks via various ways. Most of the tasks require surgeon's operation directly or indirectly. Certain level of autonomy in robotic surgery could not only free the surgeon from some tedious repetitive tasks, but also utilize the advantages of robot: high dexterity and accuracy. This paper presents a semi-autonomous neurosurgical procedure of brain tumor ablation using RAVEN Surgical Robot and stereo visual feedback. By integrating with the behavior tree framework, the whole surgical task is modeled flexibly and intelligently as nodes and leaves of a behavior tree. This paper provides three contributions mainly: (1) describing the brain tumor ablation as an ideal candidate for autonomous robotic surgery, (2) modeling and implementing the semi-autonomous surgical task using behavior tree framework, and (3) designing an experimental simulated ablation task for feasibility study and robot performance analysis.

Identifiers

PMID26405563
PMCPMC4578323

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Registered trials

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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.