Literature DB >> 29564369

Search pattern training for evaluation of central venous catheter positioning on chest radiographs.

William F Auffermann1, Elizabeth A Krupinski1, Srini Tridandapani1,2.   

Abstract

The goal of this research was to examine whether search pattern training for central line positioning on chest radiographs (CXRs) improves the ability of healthcare trainees and practitioners to identify malpositioned central venous catheters. Two sets of CXRs with central catheters were shown; half of the images contained catheters that were appropriately positioned, half that were malpositioned. Subjects were asked to: mark the tip of the catheter using the simulated radiology workstations, indicate their confidence in tip localization, and state whether the catheter was appropriately positioned or malpositioned. Subjects were also given a survey assessing their thoughts about the usefulness of search pattern training and the simulated radiology workstation. There was a significant improvement in subjects' ability to classify a catheter as malpositioned after training, [Formula: see text]-value = 0.03. There was no significant difference in localization of the catheter tips or in the confidence for tip localization. Subjects' responses to the questionnaire were significantly positive for all statements, indicating that they felt search pattern training using a simulated radiology workstation had a positive impact on their education. These results suggest that our knowledge of medical image perception may be useful for developing rational educational tools for image interpretation, and that simulated radiology workstations may be a helpful means of deploying these tools.

Entities:  

Keywords:  education; imaging; perception; radiology; training

Year:  2018        PMID: 29564369      PMCID: PMC5852256          DOI: 10.1117/1.JMI.5.3.031407

Source DB:  PubMed          Journal:  J Med Imaging (Bellingham)        ISSN: 2329-4302


  15 in total

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Authors:  Harold L Kundel
Journal:  J Am Coll Radiol       Date:  2006-06       Impact factor: 5.532

5.  Teaching Systematic Viewing to Final-Year Medical Students Improves Systematicity but Not Coverage or Detection of Radiologic Abnormalities.

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Journal:  J Am Coll Radiol       Date:  2017-02       Impact factor: 5.532

Review 6.  Statistical grand rounds: a review of analysis and sample size calculation considerations for Wilcoxon tests.

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Journal:  Minerva Anestesiol       Date:  2005-09       Impact factor: 3.051

Review 8.  Current perspectives in medical image perception.

Authors:  Elizabeth A Krupinski
Journal:  Atten Percept Psychophys       Date:  2010-07       Impact factor: 2.199

9.  [Insertion and management of long-term central venous devices: role of radiologic imaging techniques].

Authors:  L Capaccioli; M Nistri; V Distante; M Rontini; A Manetti; A Stecco
Journal:  Radiol Med       Date:  1998-10       Impact factor: 3.469

10.  The incidence and risk of central venous catheter malpositioning: a prospective cohort study in 1619 patients.

Authors:  A Pikwer; L Bååth; B Davidson; I Perstoft; J Akeson
Journal:  Anaesth Intensive Care       Date:  2008-01       Impact factor: 1.669

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  5 in total

1.  Cognitive processing differences of experts and novices when correlating anatomy and cross-sectional imaging.

Authors:  Lonie R Salkowski; Rosemary Russ
Journal:  J Med Imaging (Bellingham)       Date:  2018-05-18

2.  Perceptual training: learning versus attentional shift.

Authors:  Soham Banerjee; Trafton Drew; Megan K Mills; William F Auffermann
Journal:  J Med Imaging (Bellingham)       Date:  2019-12-31

3.  RadSimPE - a Radiology Workstation Simulator for Perceptual Education.

Authors:  Soham Banerjee; William F Auffermann
Journal:  J Digit Imaging       Date:  2021-07-29       Impact factor: 4.903

4.  Eye Tracking for Deep Learning Segmentation Using Convolutional Neural Networks.

Authors:  J N Stember; H Celik; E Krupinski; P D Chang; S Mutasa; B J Wood; A Lignelli; G Moonis; L H Schwartz; S Jambawalikar; U Bagci
Journal:  J Digit Imaging       Date:  2019-08       Impact factor: 4.056

5.  The Oddity Detection in Diverse Scenes (ODDS) database: Validated real-world scenes for studying anomaly detection.

Authors:  Michael C Hout; Megan H Papesh; Saleem Masadeh; Hailey Sandin; Stephen C Walenchok; Phillip Post; Jessica Madrid; Bryan White; Juan D Guevara Pinto; Julian Welsh; Dre Goode; Rebecca Skulsky; Mariana Cazares Rodriguez
Journal:  Behav Res Methods       Date:  2022-03-30
  5 in total

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