Literature DB >> 26870749

Teaching search patterns to medical trainees in an educational laboratory to improve perception of pulmonary nodules.

William F Auffermann1, Brent P Little1, Srini Tridandapani2.   

Abstract

The goal of this research is to demonstrate that teaching healthcare trainees a formal search or scan pattern for evaluation of the lungs improves their ability to identify pulmonary nodules on chest radiographs (CXRs). A group of physician assistant trainees were randomly assigned to control and experimental groups. Each group was shown two sets of CXRs, each set with a nodule prevalence of approximately 50%. The experimental group received search pattern training between case sets, whereas the control group did not. Both groups were asked to mark nodules when present and indicate their diagnostic confidence. Subject performance at nodule detection was quantified using changes in area under the localization receiver operating characteristic curve ([Formula: see text]). There was no significant improvement in performance between case sets for the control group. There was a significant improvement in subject performance after training for the experimental group, [Formula: see text], [Formula: see text]. These results demonstrate that teaching a search pattern to trainees improves their ability to identify nodules and decreases the number of perceptual errors in nodule identification, and suggest that our knowledge of medical image perception may be used to develop rational tools for the education of healthcare trainees.

Keywords:  education; imaging; perception; radiology; training

Year:  2015        PMID: 26870749      PMCID: PMC4748144          DOI: 10.1117/1.JMI.3.1.011006

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


  16 in total

1.  Simulation of nodules and diffuse infiltrates in chest radiographs using CT templates.

Authors:  G J S Litjens; L Hogeweg; A M R Schilham; P A de Jong; M A Viergever; B van Ginneken
Journal:  Med Image Comput Comput Assist Interv       Date:  2010

2.  A software tool for increased efficiency in observer performance studies in radiology.

Authors:  Sara Börjesson; Markus Håkansson; Magnus Båth; Susanne Kheddache; Sune Svensson; Anders Tingberg; Anna Grahn; Mark Ruschin; Bengt Hemdal; Sören Mattsson; Lars Gunnar Månsson
Journal:  Radiat Prot Dosimetry       Date:  2005       Impact factor: 0.972

3.  Simulation in healthcare education: a best evidence practical guide. AMEE Guide No. 82.

Authors:  Ivette Motola; Luke A Devine; Hyun Soo Chung; John E Sullivan; S Barry Issenberg
Journal:  Med Teach       Date:  2013-08-13       Impact factor: 3.650

4.  Dorfman-Berbaum-Metz method for statistical analysis of multireader, multimodality receiver operating characteristic data: validation with computer simulation.

Authors:  C A Roe; C E Metz
Journal:  Acad Radiol       Date:  1997-04       Impact factor: 3.173

Review 5.  Statistical grand rounds: a review of analysis and sample size calculation considerations for Wilcoxon tests.

Authors:  George Divine; H James Norton; Ronald Hunt; Jacqueline Dienemann
Journal:  Anesth Analg       Date:  2013-03-01       Impact factor: 5.108

6.  Visual search patterns and experience with radiological images.

Authors:  H L Kundel; P S La Follette
Journal:  Radiology       Date:  1972-06       Impact factor: 11.105

7.  Searching for lung nodules. A comparison of human performance with random and systematic scanning models.

Authors:  H L Kundel; C F Nodine; D Thickman; L Toto
Journal:  Invest Radiol       Date:  1987-05       Impact factor: 6.016

8.  Comparison scans while reading chest images. Taught, but not practiced.

Authors:  D P Carmody; H L Kundel; L C Toto
Journal:  Invest Radiol       Date:  1984 Sep-Oct       Impact factor: 6.016

9.  Chest "gestalt" and detectability of lung lesions.

Authors:  J W Oestmann; R Greene; P M Bourgouin; L Linetsky; H J Llewellyn
Journal:  Eur J Radiol       Date:  1993-02       Impact factor: 3.528

Review 10.  The chest radiograph.

Authors:  Barry Kelly
Journal:  Ulster Med J       Date:  2012-09
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  8 in total

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

Authors:  William F Auffermann; Elizabeth A Krupinski; Srini Tridandapani
Journal:  J Med Imaging (Bellingham)       Date:  2018-03-15

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.  Training focal lung pathology detection using an eye movement modeling example.

Authors:  Stephanie Brams; Gal Ziv; Ignace Tc Hooge; Oron Levin; Johny Verschakelen; A Mark Williams; Johan Wagemans; Werner F Helsen
Journal:  J Med Imaging (Bellingham)       Date:  2021-03-13

4.  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

5.  What We Do and Do Not Know about Teaching Medical Image Interpretation.

Authors:  Ellen M Kok; Koos van Geel; Jeroen J G van Merriënboer; Simon G F Robben
Journal:  Front Psychol       Date:  2017-03-03

6.  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

7.  Using aversive conditioning with near-real-time feedback to shape eye movements during naturalistic viewing.

Authors:  Brian A Anderson
Journal:  Behav Res Methods       Date:  2020-09-11

8.  Examining the effects of passive and active strategies on behavior during hybrid visual memory search: evidence from eye tracking.

Authors:  Jessica Madrid; Michael C Hout
Journal:  Cogn Res Princ Implic       Date:  2019-09-23
  8 in total

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