Literature DB >> 35911209

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

Jeremy M Wolfe1,2, Wanyi Lyu1, Jeffrey Dong3, Chia-Chien Wu1,2.   

Abstract

Purpose: Automated breast ultrasound (ABUS) presents three-dimensional (3D) representations of the breast in the form of stacks of coronal and transverse plane images. ABUS is especially useful for the assessment of dense breasts. Here, we present the first eye tracking data showing how radiologists search and evaluate ABUS cases. Approach: Twelve readers evaluated single-breast cases in 20-min sessions. Positive findings were present in 56% of the evaluated cases. Eye position and the currently visible coronal and transverse slice were tracked, allowing for reconstruction of 3D "scanpaths."
Results: Individual readers had consistent search strategies. Most readers had strategies that involved examination of all available images. Overall accuracy was 0.74 (sensitivity = 0.66 and specificity = 0.84). The 20 false negative errors across all readers can be classified using Kundel's (1978) taxonomy: 17 are "decision" errors (readers found the target but misclassified it as normal or benign). There was one recognition error and two "search" errors. This is an unusually high proportion of decision errors. Readers spent essentially the same proportion of time viewing coronal and transverse images, regardless of whether the case was positive or negative, correct or incorrect. Readers tended to use a "scanner" strategy when viewing coronal images and a "driller" strategy when viewing transverse images. Conclusions: These results suggest that ABUS errors are more likely to be errors of interpretation than of search. Further research could determine if readers' exploration of all images is useful or if, in some negative cases, search of transverse images is redundant following a search of coronal images.
© 2022 Society of Photo-Optical Instrumentation Engineers (SPIE).

Entities:  

Keywords:  breast ultrasound; eye tracking; mammography; medical error; visual search

Year:  2022        PMID: 35911209      PMCID: PMC9315059          DOI: 10.1117/1.JMI.9.4.045502

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


  21 in total

1.  Scanners and drillers: characterizing expert visual search through volumetric images.

Authors:  Trafton Drew; Melissa Le-Hoa Vo; Alex Olwal; Francine Jacobson; Steven E Seltzer; Jeremy M Wolfe
Journal:  J Vis       Date:  2013-08-06       Impact factor: 2.240

2.  Comparing search patterns in digital breast tomosynthesis and full-field digital mammography: an eye tracking study.

Authors:  Avi Aizenman; Trafton Drew; Krista A Ehinger; Dianne Georgian-Smith; Jeremy M Wolfe
Journal:  J Med Imaging (Bellingham)       Date:  2017-10-27

Review 3.  Ultrasound Imaging Technologies for Breast Cancer Detection and Management: A Review.

Authors:  Rongrong Guo; Guolan Lu; Binjie Qin; Baowei Fei
Journal:  Ultrasound Med Biol       Date:  2017-10-26       Impact factor: 2.998

4.  Visual search in breast imaging.

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

Review 5.  Optimizing analysis, visualization, and navigation of large image data sets: one 5000-section CT scan can ruin your whole day.

Authors:  Katherine P Andriole; Jeremy M Wolfe; Ramin Khorasani; S Ted Treves; David J Getty; Francine L Jacobson; Michael L Steigner; John J Pan; Arkadiusz Sitek; Steven E Seltzer
Journal:  Radiology       Date:  2011-05       Impact factor: 11.105

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

Authors:  Jeremy M Wolfe; Chia-Chien Wu; Jonathan Li; Sneha B Suresh
Journal:  J Med Imaging (Bellingham)       Date:  2021-07-13

7.  A 'snapshot' of the visual search behaviours of medical sonographers.

Authors:  Ann J Carrigan; Patrick C Brennan; Mariusz Pietrzyk; Jillian Clarke; Eugene Chekaluk
Journal:  Australas J Ultrasound Med       Date:  2015-12-31

Review 8.  Automated Breast Ultrasound Screening for Dense Breasts.

Authors:  Sung Hun Kim; Hak Hee Kim; Woo Kyung Moon
Journal:  Korean J Radiol       Date:  2020-01       Impact factor: 3.500

Review 9.  Early detection of breast cancer: benefits and risks of supplemental breast ultrasound in asymptomatic women with mammographically dense breast tissue. A systematic review.

Authors:  Monika Nothacker; Volker Duda; Markus Hahn; Mathias Warm; Friedrich Degenhardt; Helmut Madjar; Susanne Weinbrenner; Ute-Susann Albert
Journal:  BMC Cancer       Date:  2009-09-20       Impact factor: 4.430

10.  Even if I showed you where you looked, remembering where you just looked is hard.

Authors:  Ellen M Kok; Avi M Aizenman; Melissa L-H Võ; Jeremy M Wolfe
Journal:  J Vis       Date:  2017-10-01       Impact factor: 2.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.