Literature DB >> 33732754

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

Stephanie Brams1, Gal Ziv2, Ignace Tc Hooge3, Oron Levin1, Johny Verschakelen4, A Mark Williams5, Johan Wagemans6, Werner F Helsen1.   

Abstract

Purpose: Published reports suggest that nonoptimal visual search behavior is associated with false negatives in chest x-ray interpretation. Eye movement modeling example (EMME)-based training interventions, that is, interventions showing models of visual search to trainees, have been shown to improve visual search as well as task accuracy. Approach: We examined the detection of focal lung pathology on chest x-rays before and after two different EMME training interventions that have been shown to be efficient: (i) an EMME showing moving fixations on a blurred background (spotlight group) and (ii) an EMME showing moving fixations on a nonblurred background (circle group). These two experimental groups were compared to a control group that was only provided with the correct location of pathologies on the chest x-rays.
Results: Performance outcomes showed improved detection sensitivity and specificity in all groups (also the control group). It appears that repetitive exposure to pathologies on chest x-rays with feedback resulted in enhanced pattern recognition. In addition, visual search strategies became more efficient during post-tests.
Conclusion: Repetitive exposure to a focal lung pathology detection task with feedback improves overall performance. However, the specific EMME training interventions did not add any further advantages. Similar training interventions can be provided online to assess feasibility and reproducibility of such (or similar) training formats. Such training can, for example, reduce the number of false negative errors, especially for novices.
© 2021 Society of Photo-Optical Instrumentation Engineers (SPIE).

Entities:  

Keywords:  image analysis; information processing; medical imaging; pattern recognition; vision; x-ray

Year:  2021        PMID: 33732754      PMCID: PMC7955141          DOI: 10.1117/1.JMI.8.2.025501

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


  40 in total

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

5.  Expertise under the microscope: processing histopathological slides.

Authors:  Thomas Jaarsma; Halszka Jarodzka; Marius Nap; Jeroen J G van Merrienboer; Henny P A Boshuizen
Journal:  Med Educ       Date:  2014-03       Impact factor: 6.251

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7.  Understanding and Confronting Our Mistakes: The Epidemiology of Error in Radiology and Strategies for Error Reduction.

Authors:  Michael A Bruno; Eric A Walker; Hani H Abujudeh
Journal:  Radiographics       Date:  2015-10       Impact factor: 5.333

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9.  The gist of the abnormal: above-chance medical decision making in the blink of an eye.

Authors:  Karla K Evans; Diane Georgian-Smith; Rosemary Tambouret; Robyn L Birdwell; Jeremy M Wolfe
Journal:  Psychon Bull Rev       Date:  2013-12

10.  Perceptual training to improve hip fracture identification in conventional radiographs.

Authors:  Weijia Chen; David HolcDorf; Mark W McCusker; Frank Gaillard; Piers D L Howe
Journal:  PLoS One       Date:  2017-12-21       Impact factor: 3.240

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