Evidence map›Paper›PMID 32430693›Full record

ArticleInternational journal of computer assisted radiology and surgery2020

A multi-camera, multi-view system for training and skill assessment for robot-assisted surgery.

Alaa Eldin Abdelaal, Apeksha Avinash, Megha Kalia, Gregory D Hager, Septimiu E Salcudean

Abstract read
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In one paragraph

Article in International journal of computer assisted radiology and surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 2 pooled it
1.6field-weighted citation impact, top 16% 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

4 citing papers in PubMed, 2 syntheses or guidelines pooled it, 10 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. 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 at 2 institutions in 2 countries.

Alaa Eldin AbdelaalElectrical and Computer Engineering Department, University of British Columbia, Vancouver, V6T 1Z4, Canada. aabdelaal@ece.ubc.ca.ORCID http://orcid.org/0000-0002-0770-2940
Apeksha AvinashElectrical and Computer Engineering Department, University of British Columbia, Vancouver, V6T 1Z4, Canada.ORCID http://orcid.org/0000-0002-2423-0826
Megha KaliaElectrical and Computer Engineering Department, University of British Columbia, Vancouver, V6T 1Z4, Canada.
Gregory D HagerDepartment of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA.
Septimiu E SalcudeanElectrical and Computer Engineering Department, University of British Columbia, Vancouver, V6T 1Z4, Canada.
University of British Columbia · CAJohns Hopkins University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThis paper introduces the concept of using an additional intracorporeal camera for the specific goal of training and skill assessment and explores the benefits of such an approach. This additional camera can provide an additional view of the surgical scene, and we hypothesize that this additional view would improve surgical training and skill assessment in robot-assisted surgery.

methodsWe developed a multi-camera, multi-view system, and we conducted two user studies ([Formula: see text]) to evaluate its effectiveness for training and skill assessment. In the training user study, subjects were divided into two groups: a single-view group and a dual-view group. The skill assessment study was a within-subject study, in which every subject was shown single- and dual view recorded videos of a surgical training task, and the goal was to count the number of errors committed in each video.

resultsThe results show the effectiveness of using an additional intracorporeal camera view for training and skill assessment. The benefits of this view are modest for skill assessment as it improves the assessment accuracy by approximately 9%. For training, the additional camera view is clearly more effective. Indeed, the dual-view group is 57% more accurate than the single-view group in a retention test. In addition, the dual-view group is 35% more accurate and 25% faster than the single-view group in a transfer test.

conclusionA multi-camera, multi-view system has the potential to significantly improve training and moderately improve skill assessment in robot-assisted surgery. One application of our work is to include an additional camera view in existing virtual reality surgical training simulators to realize its benefits in training. The views from the additional intracorporeal camera can also be used to improve on existing surgical skill assessment criteria used in training systems for robot-assisted surgery.

Indexed as

Clinical CompetenceRobotic Surgical ProceduresHumansVirtual RealityMinimally invasive surgeryMulti-camera systemRobot-assisted surgerySurgical roboticsSurgical skill assessmentSurgical training

Identifiers

PMID32430693
OpenAlexW3027845585

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.