Evidence map›Paper›PMID 31341513›Full record

ArticleApplied bionics and biomechanics2019

Real-Time Needle Force Modeling for VR-Based Renal Biopsy Training with Respiratory Motion Using Direct Clinical Data.

Feiyan Li, Yonghang Tai, Qiong Li, Jun Peng, Xiaoqiao Huang, Zaiqing Chen, Junsheng Shi

Abstract read
In one paragraph

Article in Applied bionics and biomechanics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Simulation-based learning in nephrology.Clinical kidney journal · 2024
    Review
  2. 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

7 authors.

Feiyan LiYunnan Key Laboratory of Opto-electronic Information Technology, Yunnan Normal University, Kunming, China.
Yonghang TaiYunnan Key Laboratory of Opto-electronic Information Technology, Yunnan Normal University, Kunming, China.ORCID https://orcid.org/0000-0001-9186-475X
Qiong LiYunnan Key Laboratory of Opto-electronic Information Technology, Yunnan Normal University, Kunming, China.
Jun PengDepartment of Urology Surgery, Yunnan First People's Hospital, Kunming, China.
Xiaoqiao HuangYunnan Key Laboratory of Opto-electronic Information Technology, Yunnan Normal University, Kunming, China.
Zaiqing ChenYunnan Key Laboratory of Opto-electronic Information Technology, Yunnan Normal University, Kunming, China.
Junsheng ShiYunnan Key Laboratory of Opto-electronic Information Technology, Yunnan Normal University, Kunming, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Realistic tool-tissue interactive modeling has been recognized as an essential requirement in the training of virtual surgery. A virtual basic surgical training framework integrated with real-time force rendering has been recognized as one of the most immersive implementations in medical education. Yet, compared to the original intraoperative data, there has always been an argument that these data are represented by lower fidelity in virtual surgical training. In this paper, a dynamic biomechanics experimental framework is designed to achieve a highly immersive haptic sensation during the biopsy therapy with human respiratory motion; it is the first time to introduce the idea of periodic extension idea into the dynamic percutaneous force modeling. Clinical evaluation is conducted and performed in the Yunnan First People's Hospital, which not only demonstrated a higher fitting degree (AVG: 99.36%) with the intraoperation data than previous algorithms (AVG: 87.83%, 72.07%, and 66.70%) but also shows a universal fitting range with multilayer tissue. 27 urologists comprising 18 novices and 9 professors were invited to the VR-based training evaluation based on the proposed haptic rendering solution. Subjective and objective results demonstrated higher performance than the existing benchmark training simulator. Combining these in a systematic approach, tuned with specific fidelity requirements, haptically enabled medical simulation systems would be able to provide a more immersive and effective training environment.

Identifiers

PMID31341513
PMCPMC6614959

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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.