Evidence map›Paper›PMID 37597089›Full record

ArticleInternational journal of computer assisted radiology and surgery2023

Surgical tooltip motion metrics assessment using virtual marker: an objective approach to skill assessment for minimally invasive surgery.

Farzad Aghazadeh, Bin Zheng, Mahdi Tavakoli, Hossein Rouhani

Abstract read
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In one paragraph

Article in International journal of computer assisted radiology and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
3.5field-weighted citation impact, top 7% 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, 11 citations in OpenAlex.

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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

4 authors at 1 institution in 1 country.

Farzad AghazadehDepartment of Mechanical Engineering, 10-390 Donadeo Innovation Centre for Engineering, University of Alberta, 9211-116 Street NW, Edmonton, AB, T6G 1H9, Canada.
Bin ZhengDepartment of Surgery, University of Alberta, Edmonton, AB, Canada.
Mahdi TavakoliDepartment of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada.
Hossein RouhaniDepartment of Mechanical Engineering, 10-390 Donadeo Innovation Centre for Engineering, University of Alberta, 9211-116 Street NW, Edmonton, AB, T6G 1H9, Canada. hrouhani@ualberta.ca.ORCID http://orcid.org/0000-0002-4790-9313
University of Alberta · CA

Funding

Alberta Minister of Jobs, Economy and Innovation, Major Innovation Fund - Autonomous Systems Initiative MIF01 T4 P1
6 · The paper itself

Abstract

purposeSurgical skill assessment has primarily been performed using checklists or rating scales, which are prone to bias and subjectivity. To tackle this shortcoming, assessment of surgical tool motion can be implemented to objectively classify skill levels. Due to the challenges involved in motion tracking of surgical tooltips in minimally invasive surgeries, formerly used assessment approaches may not be feasible for real-world skill assessment. We proposed an assessment approach based on the virtual marker on surgical tooltips to derive the tooltip's 3D position and introduced a novel metric for surgical skill assessment.

methodsWe obtained the 3D tooltip position based on markers placed on the tool handle. Then, we derived tooltip motion metrics to identify the metrics differentiating the skill levels for objective surgical skill assessment. We proposed a new tooltip motion metric, i.e., motion inconsistency, that can assess the skill level, and also can evaluate the stage of skill learning. In this study, peg transfer, dual transfer, and rubber band translocation tasks were included, and nine novices, five surgical residents and five attending general surgeons participated.

resultsOur analyses showed that tooltip path length (p [Formula: see text] 0.007) and path length along the instrument axis (p [Formula: see text] 0.014) differed across the three skill levels in all the tasks and decreased by skill level. Tooltip motion inconsistency showed significant differences among the three skill levels in the dual transfer (p [Formula: see text] 0.025) and the rubber band translocation tasks (p [Formula: see text] 0.021). Lastly, bimanual dexterity differed across the three skill levels in all the tasks (p [Formula: see text] 0.012) and increased by skill level.

conclusionDepth perception ability (indicated by shorter tooltip path lengths along the instrument axis), bimanual dexterity, tooltip motion consistency, and economical tooltip movements (shorter tooltip path lengths) are related to surgical skill. Our findings can contribute to objective surgical skill assessment, reducing subjectivity, bias, and associated costs.

Indexed as

LaparoscopySurgeonsClinical CompetenceHumansMinimally Invasive Surgical ProceduresMotionMovementLaparoscopic surgeryLocal coordinate systemMinimally invasive surgeryMotion analysisObjective skill assessment

Identifiers

PMID37597089
OpenAlexW4385999585

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.