Evidence map›Paper›PMID 35802223›Full record

ArticleInternational journal of computer assisted radiology and surgery2022

Robot-assisted ex vivo neobladder reconstruction: preliminary results of surgical skill evaluation.

Ziyang Chen, Serenella Terlizzi, Tommaso Da Col, Aldo Marzullo, Michele Catellani, Giancarlo Ferrigno, Elena De Momi

Open access · hybridAbstract read
In one paragraph

Article in International journal of computer assisted radiology and surgery, 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
1.3field-weighted citation impact, top 20% of its field
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, 6 citations in OpenAlex.

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

7 authors at 3 institutions in 1 country.

Ziyang ChenDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy. ziyang.chen@polimi.it.ORCID http://orcid.org/0000-0003-3077-822X
Serenella TerlizziDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
Tommaso Da ColDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
Aldo MarzulloDepartment of Mathematics and Computer Science, University of Calabria, Rende, Italy.
Michele CatellaniIstituto Europeo di Oncologia, Milan, Italy.
Giancarlo FerrignoDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
Elena De MomiDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
Politecnico di Milano · ITEuropean Institute of Oncology · ITUniversity of Calabria · IT

Funding

Horizon 2020 732515
6 · The paper itself

Abstract

purposeAdvanced developments in the medical field have gradually increased the public demand for surgical skill evaluation. However, this assessment always depends on the direct observation of experienced surgeons, which is time-consuming and variable. The introduction of robot-assisted surgery provides a new possibility for this evaluation paradigm. This paper aims at evaluating surgeon performance automatically with novel evaluation metrics based on different surgical data.

methodsUrologists ([Formula: see text]) from a hospital were requested to perform a simplified neobladder reconstruction on an ex vivo setup twice with different camera modalities ([Formula: see text]) randomly. They were divided into novices and experts ([Formula: see text], respectively) according to their experience in robot-assisted surgeries. Different performance metrics ([Formula: see text]) are proposed to achieve the surgical skill evaluation, considering both instruments and endoscope. Also, nonparametric tests are adopted to check if there are significant differences when evaluating surgeons performance.

resultsWhen grouping according to four stages of neobladder reconstruction, statistically significant differences can be appreciated in phase 1 ([Formula: see text]) and phase 2 ([Formula: see text]) with normalized time-related metrics and camera movement-related metrics, respectively. On the other hand, considering experience grouping shows that both metrics are able to highlight statistically significant differences between novice and expert performances in the control protocol. It also shows that the camera-related performance of experts is significantly different ([Formula: see text]) when handling the endoscope manually and when it is automatic.

conclusionSurgical skill evaluation, using the approach in this paper, can effectively measure surgical procedures of surgeons with different experience. Preliminary results demonstrate that different surgical data can be fully utilized to improve the reliability of surgical evaluation. It also demonstrates its versatility and potential in the quantitative assessment of various surgical operations.

Indexed as

RoboticsRobotic Surgical ProceduresSurgeonsClinical CompetenceHumansReproducibility of ResultsPerformance metricsRobot-assisted surgeriesSignificant differencesSurgical skill evaluation

Identifiers

PMID35802223
PMCPMC9652243
OpenAlexW4284888188

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.