Literature DB >> 34277890

What do experts look at and what do experts find when reading mammograms?

Jeremy M Wolfe1,2, Chia-Chien Wu1,2, Jonathan Li3, Sneha B Suresh1.   

Abstract

Purpose: Radiologists sometimes fail to report clearly visible, clinically significant findings. Eye tracking can provide insight into the causes of such errors. Approach: We tracked eye movements of 17 radiologists, searching for masses in 80 mammograms (60 with masses).
Results: Errors were classified using the Kundel et al. (1978) taxonomy: search errors (target never fixated), recognition errors (fixated < 500    ms ), or decision errors (fixated > 500    ms ). Error proportions replicated Krupinski (1996): search 25%, recognition 25%, and decision 50%. Interestingly, we found few differences between experts and residents in accuracy or eye movement metrics. Error categorization depends on the definition of the useful field of view (UFOV) around fixation. We explored different UFOV definitions, based on targeting saccades and search saccades. Targeting saccades averaged slightly longer than search saccades. Of most interest, we found that the probability that the eyes would move to the target on the next saccade or even on one of the next three saccades was strikingly low ( ∼ 33 % , even when the eyes were < 2    deg from the target). This makes it clear that observers do not fully process everything within a UFOV. Using a probabilistic UFOV, we find, unsurprisingly, that observers cover more of the image when no target is present than when it is found. Interestingly, we do not find evidence that observers cover too little of the image on trials when they miss the target. Conclusions: These results indicate that many errors in mammography reflect failed deployment of attention; not failure to fixate clinically significant locations.
© 2021 Society of Photo-Optical Instrumentation Engineers (SPIE).

Entities:  

Keywords:  Attention; eye movements; mammogram; useful field of view; visual search

Year:  2021        PMID: 34277890      PMCID: PMC8277193          DOI: 10.1117/1.JMI.8.4.045501

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


  48 in total

1.  Nature of expertise in searching mammograms for breast masses.

Authors:  C F Nodine; H L Kundel; S C Lauver; L C Toto
Journal:  Acad Radiol       Date:  1996-12       Impact factor: 3.173

2.  Variations in the functional visual field for detection of lung nodules on chest computed tomography: Impact of nodule size, distance, and local lung complexity.

Authors:  Lukas Ebner; Martin Tall; Kingshuk Roy Choudhury; Donald L Ly; Justus E Roos; Sandy Napel; Geoffrey D Rubin
Journal:  Med Phys       Date:  2017-05-23       Impact factor: 4.071

3.  Uncertainty modeling for ontology-based mammography annotation with intelligent BI-RADS scoring.

Authors:  Hakan Bulu; Adil Alpkocak; Pinar Balci
Journal:  Comput Biol Med       Date:  2013-02-14       Impact factor: 4.589

4.  Measurement of the useful field of view for single slices of different imaging modalities and targets.

Authors:  Miguel A Lago; Ioannis Sechopoulos; François O Bochud; Miguel P Eckstein
Journal:  J Med Imaging (Bellingham)       Date:  2020-02-08

5.  Visual search in breast imaging.

Authors:  Ziba Gandomkar; Claudia Mello-Thoms
Journal:  Br J Radiol       Date:  2019-07-18       Impact factor: 3.039

Review 6.  Visual search in scenes involves selective and nonselective pathways.

Authors:  Jeremy M Wolfe; Melissa L-H Võ; Karla K Evans; Michelle R Greene
Journal:  Trends Cogn Sci       Date:  2011-01-10       Impact factor: 20.229

7.  Factors affecting radiologist inconsistency in screening mammography.

Authors:  Craig A Beam; Emily F Conant; Edward A Sickles
Journal:  Acad Radiol       Date:  2002-05       Impact factor: 3.173

8.  Breast Cancer Screening for Average-Risk Women: Recommendations From the ACR Commission on Breast Imaging.

Authors:  Debra L Monticciolo; Mary S Newell; R Edward Hendrick; Mark A Helvie; Linda Moy; Barbara Monsees; Daniel B Kopans; Peter R Eby; Edward A Sickles
Journal:  J Am Coll Radiol       Date:  2017-06-22       Impact factor: 5.532

9.  Analysis of cancers missed at screening mammography.

Authors:  R E Bird; T W Wallace; B C Yankaskas
Journal:  Radiology       Date:  1992-09       Impact factor: 11.105

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  3 in total

1.  The Functional Visual Field(s) in simple visual search.

Authors:  Chia-Chien Wu; Jeremy M Wolfe
Journal:  Vision Res       Date:  2021-11-11       Impact factor: 1.886

2.  What eye tracking can tell us about how radiologists use automated breast ultrasound.

Authors:  Jeremy M Wolfe; Wanyi Lyu; Jeffrey Dong; Chia-Chien Wu
Journal:  J Med Imaging (Bellingham)       Date:  2022-07-26

3.  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
  3 in total

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