Evidence map›Paper›PMID 36547561›Full record

ArticleJournal of functional biomaterials2022

A High-Fidelity Artificial Urological System for the Quantitative Assessment of Endoscopic Skills.

Do Yeon Kim, Xiangzhou Tan, Moonkwang Jeong, Dandan Li, Arkadiusz Miernik, Tian Qiu

Abstract read
In one paragraph

Article in Journal of functional biomaterials, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Do Yeon KimCyber Valley Group-Biomedical Microsystems, Institute of Physical Chemistry, University of Stuttgart, 70569 Stuttgart, Germany.
Xiangzhou TanCyber Valley Group-Biomedical Microsystems, Institute of Physical Chemistry, University of Stuttgart, 70569 Stuttgart, Germany.
Moonkwang JeongCyber Valley Group-Biomedical Microsystems, Institute of Physical Chemistry, University of Stuttgart, 70569 Stuttgart, Germany.ORCID 0000-0003-3731-1833
Dandan LiCyber Valley Group-Biomedical Microsystems, Institute of Physical Chemistry, University of Stuttgart, 70569 Stuttgart, Germany.
Arkadiusz MiernikDepartment of Urology, Faculty of Medicine, Medical Center-University of Freiburg, 79106 Freiburg, Germany.ORCID 0000-0001-5894-9647
Tian QiuCyber Valley Group-Biomedical Microsystems, Institute of Physical Chemistry, University of Stuttgart, 70569 Stuttgart, Germany.ORCID 0000-0003-0932-5605

Funding

Cyber Valley Research Fund CyVy-RF-2020-09University of Freiburg Medical Centre DATES-RF-2021-12Vector Foundation P2017-0065
6 · The paper itself

Abstract

Minimally-invasive surgery is rapidly growing and has become a standard approach for many operations. However, it requires intensive practice to achieve competency. The current training often relies on animal organ models or physical organ phantoms, which do not offer realistic surgical scenes or useful real-time feedback for surgeons to improve their skills. Furthermore, the objective quantitative assessment of endoscopic skills is also lacking. Here, we report a high-fidelity artificial urological system that allows realistic simulation of endourological procedures and offers a quantitative assessment of the surgical performance. The physical organ model was fabricated by 3D printing and two-step polymer molding with the use of human CT data. The system resembles the human upper urinary tract with a high-resolution anatomical shape and vascular patterns. During surgical simulation, endoscopic videos are acquired and analyzed to quantitatively evaluate performance skills by a customized computer algorithm. Experimental results show significant differences in the performance between professional surgeons and trainees. The surgical simulator offers a unique chance to train endourological procedures in a realistic and safe environment, and it may also lead to a quantitative standard to evaluate endoscopic skills.

Indexed as

additive manufacturingdata sensing in bio-modelsendoscopyorgan phantomssurgical simulation

Identifiers

PMID36547561
PMCPMC9784860

What OpenQuestion holds

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LicenceCC BY
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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.