Evidence map›Paper›PMID 39460860›Full record

ArticleInternational journal of computer assisted radiology and surgery2025

Semi-automatic robotic puncture system based on deformable soft tissue point cloud registration.

Bo Zhang, Kui Chen, Yuhang Yao, Bo Wu, Qiang Li, Zheming Zhang, Peihua Fan, Wei Wang, Manxia Lin, Masakatsu G Fujie

Abstract read
In one paragraph

Article in International journal of computer assisted radiology and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Semi-automatic puncture robotic system based on real-time multi-modal image fusion: preclinical evaluation.International journal of computer assisted radiology and surgery · 2025
    Article
  6. Real-time 3D US-CT fusion-based semi-automatic puncture robot system: clinical evaluation.International journal of computer assisted radiology and surgery · 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

10 authors.

Bo ZhangWuXi AMIT Intelligent Medical Technology Col., Ltd., Wuxi, 214000, China.
Kui ChenWuXi AMIT Intelligent Medical Technology Col., Ltd., Wuxi, 214000, China. chenkui@amit.net.cn.ORCID http://orcid.org/0000-0002-7473-5697
Yuhang YaoWuXi AMIT Intelligent Medical Technology Col., Ltd., Wuxi, 214000, China.
Bo WuWuXi AMIT Intelligent Medical Technology Col., Ltd., Wuxi, 214000, China.
Qiang LiWuXi AMIT Intelligent Medical Technology Col., Ltd., Wuxi, 214000, China.
Zheming ZhangWuXi AMIT Intelligent Medical Technology Col., Ltd., Wuxi, 214000, China.
Peihua FanWuXi AMIT Intelligent Medical Technology Col., Ltd., Wuxi, 214000, China.
Wei WangThe First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510000, China.
Manxia LinThe First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510000, China.
Masakatsu G FujieFuture Robotics Organization, Waseda University, Tokyo, 1620044, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTraditional surgical puncture robot systems based on computed tomography (CT) and infrared camera guidance have natural disadvantages for puncture of deformable soft tissues such as the liver. Liver movement and deformation caused by breathing are difficult to accurately assess and compensate by current technical solutions. We propose a semi-automatic robotic puncture system based on real-time ultrasound images to solve this problem.

methodReal-time ultrasound images and their spatial position information can be obtained by robot in this system. By recognizing target tissue in these ultrasound images and using reconstruction algorithm, 3D real-time ultrasound tissue point cloud can be constructed. Point cloud of the target tissue in the CT image can be obtained by using developed software. Through the point cloud registration method based on feature points, two point clouds above are registered. The puncture target will be automatically positioned, then robot quickly carries the puncture guide mechanism to the puncture site and guides the puncture. It takes about just tens of seconds from the start of image acquisition to completion of needle insertion. Patient can be controlled by a ventilator to temporarily stop breathing, and patient's breathing state does not need to be the same as taking CT scan.

resultsThe average operation time of 24 phantom experiments is 64.5 s, and the average error between the needle tip and the target point after puncture is 0.8 mm. Two animal puncture surgeries were performed, and the results indicated that the puncture errors of these two experiments are 1.76 mm and 1.81 mm, respectively.

conclusionRobot system can effectively carry out and implement liver tissue puncture surgery, and the success rate of phantom experiments and experiments is 100%. It also shows that the puncture robot system has high puncture accuracy, short operation time, and great clinical value.

Indexed as

LiverPuncturesRobotic Surgical ProceduresSurgery, Computer-AssistedAlgorithmsAnimalsEquipment DesignHumansImaging, Three-DimensionalPhantoms, ImagingTomography, X-Ray ComputedUltrasonographyImage recognitionPoint cloud registrationPuncture systemSurgical robotThree-dimensional reconstruction

Identifiers

PMID39460860
PMCPMC11929701

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.