Evidence map›Paper›PMID 34750700›Full record

SynthesisSurgical endoscopy2022

Review of automated performance metrics to assess surgical technical skills in robot-assisted laparoscopy.

Sonia Guerin, Arnaud Huaulmé, Vincent Lavoue, Pierre Jannin, Krystel Nyangoh Timoh

Abstract readSystematic Review
PubMed Publisher
In one paragraph

Synthesis in Surgical endoscopy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 2 pooled it
–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

16 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Review
  5. Training and credentialing in robotic general surgery.International journal of colorectal disease · 2026
    Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Neuroendoscopy Training.Asian journal of neurosurgery · 2025
    Review
  11. Automated assessment of non-technical skills by heart-rate data.International journal of computer assisted radiology and surgery · 2025
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. 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

5 authors.

Sonia GuerinDepartment of Gynaecology Obstetrics, Rennes University Hospital, Univ Rennes, INSERM, LTSI - UMR 1099, F35000, Rennes, France. soniaguerin26@gmail.com.ORCID 0000-0002-7275-4261
Arnaud HuaulméUniversity Rennes, INSERM, LTSI - UMR 1099, F35000, Rennes, France.
Vincent LavoueDepartment of Gynaecology Obstetrics, Rennes University Hospital, Univ Rennes, INSERM, LTSI - UMR 1099, F35000, Rennes, France.
Pierre JanninUniversity Rennes, INSERM, LTSI - UMR 1099, F35000, Rennes, France.
Krystel Nyangoh TimohDepartment of Gynaecology Obstetrics, Rennes University Hospital, Univ Rennes, INSERM, LTSI - UMR 1099, F35000, Rennes, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionRobot-assisted laparoscopy is a safe surgical approach with several studies suggesting correlations between complication rates and the surgeon's technical skills. Surgical skills are usually assessed by questionnaires completed by an expert observer. With the advent of surgical robots, automated surgical performance metrics (APMs)-objective measures related to instrument movements-can be computed. The aim of this systematic review was thus to assess APMs use in robot-assisted laparoscopic procedures. The primary outcome was the assessment of surgical skills by APMs and the secondary outcomes were the association between APM and surgeon parameters and the prediction of clinical outcomes.

methodsA systematic review following the PRISMA guidelines was conducted. PubMed and Scopus electronic databases were screened with the query "robot-assisted surgery OR robotic surgery AND performance metrics" between January 2010 and January 2021. The quality of the studies was assessed by the medical education research study quality instrument. The study settings, metrics, and applications were analysed.

resultsThe initial search yielded 341 citations of which 16 studies were finally included. The study settings were either simulated virtual reality (VR) (4 studies) or real clinical environment (12 studies). Data to compute APMs were kinematics (motion tracking), and system and specific events data (actions from the robot console). APMs were used to differentiate expertise levels, and thus validate VR modules, predict outcomes, and integrate datasets for automatic recognition models. APMs were correlated with clinical outcomes for some studies.

conclusionsAPMs constitute an objective approach for assessing technical skills. Evidence of associations between APMs and clinical outcomes remain to be confirmed by further studies, particularly, for non-urological procedures. Concurrent validation is also required.

Indexed as

LaparoscopyRoboticsRobotic Surgical ProceduresVirtual RealityBenchmarkingClinical CompetenceHumansAutomated performance metricsKinematicsRobot-assisted laparoscopySurgical assessmentSurgical technical skills

Identifiers

What OpenQuestion holds

Textmetadata
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.