Literature DB >> 27659798

Does Expectation of Abnormality Affect the Search Pattern of Radiologists When Looking for Pulmonary Nodules?

Stephen Littlefair1, Patrick Brennan2, Warren Reed2, Claudia Mello-Thoms2.   

Abstract

This experiment investigated whether there might be an effect on the visual search strategy of radiologists during image interpretation of the same adult chest radiographs when given different clinical information. Each of 17 experienced radiologists was asked to interpret a set of 57 (10 abnormal) posteroanterior chest images to identify the presence of pulmonary lesions using differing clinical information (leading to unknown, low and high expectations of prevalence). Eye position metrics (search time, dwell time and time to first fixation) were compared for normal and abnormal images, as well as between conditions. For all images, there was a significantly longer search time at high prevalence expectation compared to low prevalence expectation (W = 75.19, P = <0.0001). Mann-Whitney analysis of the abnormal images demonstrated that the dwell time on correctly identified lesions was significantly shorter at low prevalence expectation compared to both unknown (U = 364.5, P = 0.02) and high prevalence expectation (U = 397.0, P = 0.0002). Visual search patterns of radiologists appear to be affected by changing a priori information where such information fosters an expectation of abnormality.

Entities:  

Keywords:  Eye-tracking; Lung; Nodule; Search

Mesh:

Year:  2017        PMID: 27659798      PMCID: PMC5267600          DOI: 10.1007/s10278-016-9908-7

Source DB:  PubMed          Journal:  J Digit Imaging        ISSN: 0897-1889            Impact factor:   4.056


  28 in total

1.  Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists' detection of pulmonary nodules.

Authors:  J Shiraishi; S Katsuragawa; J Ikezoe; T Matsumoto; T Kobayashi; K Komatsu; M Matsui; H Fujita; Y Kodera; K Doi
Journal:  AJR Am J Roentgenol       Date:  2000-01       Impact factor: 3.959

2.  On the choice of acceptance radius in free-response observer performance studies.

Authors:  T M Haygood; J Ryan; P C Brennan; S Li; E M Marom; M F McEntee; M Itani; M Evanoff; D Chakraborty
Journal:  Br J Radiol       Date:  2012-05-09       Impact factor: 3.039

3.  Visual scanning patterns of radiologists searching mammograms.

Authors:  E A Krupinski
Journal:  Acad Radiol       Date:  1996-02       Impact factor: 3.173

4.  The effect of abnormality-prevalence expectation on expert observer performance and visual search.

Authors:  Warren M Reed; John T Ryan; Mark F McEntee; Michael G Evanoff; Patrick C Brennan
Journal:  Radiology       Date:  2011-01-19       Impact factor: 11.105

5.  Omissions in radiology: faulty search or stringent reporting criteria?

Authors:  R G Swensson; S J Hessel; P G Herman
Journal:  Radiology       Date:  1977-06       Impact factor: 11.105

6.  The value of searching films without specific preconceptions.

Authors:  R G Swensson; S J Hessel; P G Herman
Journal:  Invest Radiol       Date:  1985 Jan-Feb       Impact factor: 6.016

7.  Visual expertise in paediatric neurology.

Authors:  Thomas Balslev; Halszka Jarodzka; Kenneth Holmqvist; Willem de Grave; Arno M M Muijtjens; Berit Eika; Jeroen van Merriënboer; Albert J J A Scherpbier
Journal:  Eur J Paediatr Neurol       Date:  2011-09-08       Impact factor: 3.140

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.  The influence of clinical history on visual search with single and multiple abnormalities.

Authors:  K S Berbaum; E A Franken; K L Anderson; D D Dorfman; W E Erkonen; G P Farrar; J J Geraghty; T J Gleason; M E MacNaughton; M E Phillips
Journal:  Invest Radiol       Date:  1993-03       Impact factor: 6.016

Review 10.  Expectation (and attention) in visual cognition.

Authors:  Christopher Summerfield; Tobias Egner
Journal:  Trends Cogn Sci       Date:  2009-08-27       Impact factor: 20.229

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  2 in total

1.  A collaborative computer aided diagnosis (C-CAD) system with eye-tracking, sparse attentional model, and deep learning.

Authors:  Naji Khosravan; Haydar Celik; Baris Turkbey; Elizabeth C Jones; Bradford Wood; Ulas Bagci
Journal:  Med Image Anal       Date:  2018-10-28       Impact factor: 8.545

2.  Feedback moderates the effect of prevalence on perceptual decisions.

Authors:  Wanyi Lyu; David E Levari; Makaela S Nartker; Daniel S Little; Jeremy M Wolfe
Journal:  Psychon Bull Rev       Date:  2021-06-25
  2 in total

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