Evidence map›Paper›PMID 37905207›Full record

ArticleThe International journal of robotics research2023

Toward certifiable optimal motion planning for medical steerable needles.

Mengyu Fu, Kiril Solovey, Oren Salzman, Ron Alterovitz

Abstract read
In one paragraph

Article in The International journal of robotics research, 2023. 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. Safe Start Regions for Medical Steerable Needle Automation.IEEE transactions on robotics : a publication of the IEEE Robotics and Automation Society · 2025
    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.

Mengyu FuDepartment of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.ORCID https://orcid.org/0000-0002-5237-1220
Kiril SoloveyDepartment of Electrical and Computer Engineering, Technion-Israel Institute of Technology, Haifa, Israel.ORCID https://orcid.org/0000-0003-0254-0572
Oren SalzmanDepartment of Computer Science, Technion-Israel Institute of Technology, Haifa, Israel.
Ron AlterovitzDepartment of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.ORCID https://orcid.org/0000-0002-4492-1384

Funding

Bronchoscopic Steerable Needles for Transparenchymal Access to Lung NodulesR01EB024864 · NIBIB · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ALTEROVITZ, RON · 2017 to 2021
$2.5M
NIBIB NIH HHS R01 EB024864
6 · The paper itself

Abstract

Medical steerable needles can follow 3D curvilinear trajectories to avoid anatomical obstacles and reach clinically significant targets inside the human body. Automating steerable needle procedures can enable physicians and patients to harness the full potential of steerable needles by maximally leveraging their steerability to safely and accurately reach targets for medical procedures such as biopsies. For the automation of medical procedures to be clinically accepted, it is critical from a patient care, safety, and regulatory perspective to certify the correctness and effectiveness of the planning algorithms involved in procedure automation. In this paper, we take an important step toward creating a certifiable optimal planner for steerable needles. We present an efficient, resolution-complete motion planner for steerable needles based on a novel adaptation of multi-resolution planning. This is the first motion planner for steerable needles that guarantees to compute in finite time an obstacle-avoiding plan (or notify the user that no such plan exists), under clinically appropriate assumptions. Based on this planner, we then develop the first resolution-optimal motion planner for steerable needles that further provides theoretical guarantees on the quality of the computed motion plan, that is, global optimality, in finite time. Compared to state-of-the-art steerable needle motion planners, we demonstrate with clinically realistic simulations that our planners not only provide theoretical guarantees but also have higher success rates, have lower computation times, and result in higher quality plans.

Indexed as

formal guaranteesmedical robotMotion planning

Identifiers

PMID37905207
PMCPMC10613120

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LicenceCC BY-NC
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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.