Evidence map›Paper›PMID 29894481›Full record

ArticlePloS one2018

Gestures for Picture Archiving and Communication Systems (PACS) operation in the operating room: Is there any standard?

Naveen Madapana, Glebys Gonzalez, Richard Rodgers, Lingsong Zhang, Juan P Wachs

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2018. 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
2.8field-weighted citation impact, top 9% 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, 23 citations in OpenAlex.

  1. JSSE: Joint Sequential Semantic Encoder for Zero-Shot Event Recognition.IEEE transactions on artificial intelligence · 2023
    Article
  2. JSE: Joint Semantic Encoder for zero-shot gesture learning.Pattern analysis and applications : PAA · 2022
    Article
  3. Article
  4. Agreement Study Using Gesture Description Analysis.IEEE transactions on human-machine systems · 2020
    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 1 country.

Naveen MadapanaSchool of Industrial Engineering, Purdue University, West Lafayette, Indiana, United States of America.
Glebys GonzalezSchool of Industrial Engineering, Purdue University, West Lafayette, Indiana, United States of America.ORCID 0000-0002-0181-006X
Richard RodgersGoodman Campbell Brain and Spine, Indianapolis, Indiana, United States of America.
Lingsong ZhangDepartment of Statistics, Purdue University, West Lafayette, Indiana, United States of America.
Juan P WachsSchool of Industrial Engineering, Purdue University, West Lafayette, Indiana, United States of America.ORCID 0000-0002-6425-5745
Purdue University West Lafayette · USIndiana Spine Group · US

Funding

AHRQ HHS R18 HS024887
6 · The paper itself

Abstract

objectiveGestural interfaces allow accessing and manipulating Electronic Medical Records (EMR) in hospitals while keeping a complete sterile environment. Particularly, in the Operating Room (OR), these interfaces enable surgeons to browse Picture Archiving and Communication System (PACS) without the need of delegating functions to the surgical staff. Existing gesture based medical interfaces rely on a suboptimal and an arbitrary small set of gestures that are mapped to a few commands available in PACS software. The objective of this work is to discuss a method to determine the most suitable set of gestures based on surgeon's acceptability. To achieve this goal, the paper introduces two key innovations: (a) a novel methodology to incorporate gestures' semantic properties into the agreement analysis, and (b) a new agreement metric to determine the most suitable gesture set for a PACS. MATERIALS AND

methodsThree neurosurgical diagnostic tasks were conducted by nine neurosurgeons. The set of commands and gesture lexicons were determined using a Wizard of Oz paradigm. The gestures were decomposed into a set of 55 semantic properties based on the motion trajectory, orientation and pose of the surgeons' hands and their ground truth values were manually annotated. Finally, a new agreement metric was developed, using the known Jaccard similarity to measure consensus between users over a gesture set.

resultsA set of 34 PACS commands were found to be a sufficient number of actions for PACS manipulation. In addition, it was found that there is a level of agreement of 0.29 among the surgeons over the gestures found. Two statistical tests including paired t-test and Mann Whitney Wilcoxon test were conducted between the proposed metric and the traditional agreement metric. It was found that the agreement values computed using the former metric are significantly higher (p < 0.001) for both tests.

conclusionsThis study reveals that the level of agreement among surgeons over the best gestures for PACS operation is higher than the previously reported metric (0.29 vs 0.13). This observation is based on the fact that the agreement focuses on main features of the gestures rather than the gestures themselves. The level of agreement is not very high, yet indicates a majority preference, and is better than using gestures based on authoritarian or arbitrary approaches. The methods described in this paper provide a guiding framework for the design of future gesture based PACS systems for the OR.

Indexed as

GesturesOperating RoomsClinical CompetenceElectronic Health RecordsHumansMovementNeurosurgeonsRadiology Information SystemsSoftwareUser-Computer Interface

Identifiers

PMID29894481
PMCPMC5997313
OpenAlexW2808388238

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.