Literature DB >> 22958720

When and why might a computer-aided detection (CAD) system interfere with visual search? An eye-tracking study.

Trafton Drew1, Corbin Cunningham, Jeremy M Wolfe.   

Abstract

RATIONAL AND
OBJECTIVES: Computer-aided detection (CAD) systems are intended to improve performance. This study investigates how CAD might actually interfere with a visual search task. This is a laboratory study with implications for clinical use of CAD.
METHODS: Forty-seven naive observers in two studies were asked to search for a target, embedded in 1/f(2.4) noise while we monitored their eye movements. For some observers, a CAD system marked 75% of targets and 10% of distractors, whereas other observers completed the study without CAD. In experiment 1, the CAD system's primary function was to tell observers where the target might be. In experiment 2, CAD provided information about target identity.
RESULTS: In experiment 1, there was a significant enhancement of observer sensitivity in the presence of CAD (t(22) = 4.74, P < .001), but there was also a substantial cost. Targets that were not marked by the CAD system were missed more frequently than equivalent targets in no-CAD blocks of the experiment (t(22) = 7.02, P < .001). Experiment 2 showed no behavioral benefit from CAD, but also no significant cost on sensitivity to unmarked targets (t(22) = 0.6, P = NS). Finally, in both experiments, CAD produced reliable changes in eye movements: CAD observers examined a lower total percentage of the search area than the no-CAD observers (experiment 1: t(48) = 3.05, P < .005; experiment 2: t(50) = 7.31, P < .001).
CONCLUSIONS: CAD signals do not combine with observers' unaided performance in a straightforward manner. CAD can engender a sense of certainty that can lead to incomplete search and elevated chances of missing unmarked stimuli.
Copyright © 2012 AUR. Published by Elsevier Inc. All rights reserved.

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Mesh:

Year:  2012        PMID: 22958720      PMCID: PMC3438519          DOI: 10.1016/j.acra.2012.05.013

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  18 in total

1.  How widely is computer-aided detection used in screening and diagnostic mammography?

Authors:  Vijay M Rao; David C Levin; Laurence Parker; Barbara Cavanaugh; Andrea J Frangos; Jonathan H Sunshine
Journal:  J Am Coll Radiol       Date:  2010-10       Impact factor: 5.532

2.  Current status and future directions of computer-aided diagnosis in mammography.

Authors:  Robert M Nishikawa
Journal:  Comput Med Imaging Graph       Date:  2007-03-26       Impact factor: 4.790

Review 3.  The preponderance of evidence supports computer-aided detection for screening mammography.

Authors:  Robyn L Birdwell
Journal:  Radiology       Date:  2009-10       Impact factor: 11.105

Review 4.  Can computer-aided detection be detrimental to mammographic interpretation?

Authors:  Liane E Philpotts
Journal:  Radiology       Date:  2009-10       Impact factor: 11.105

5.  Effectiveness of computer-aided detection in community mammography practice.

Authors:  Joshua J Fenton; Linn Abraham; Stephen H Taplin; Berta M Geller; Patricia A Carney; Carl D'Orsi; Joann G Elmore; William E Barlow
Journal:  J Natl Cancer Inst       Date:  2011-07-27       Impact factor: 13.506

6.  Human observer detection experiments with mammograms and power-law noise.

Authors:  A E Burgess; F L Jacobson; P F Judy
Journal:  Med Phys       Date:  2001-04       Impact factor: 4.071

7.  Using computer-aided detection in mammography as a decision support.

Authors:  Maurice Samulski; Rianne Hupse; Carla Boetes; Roel D M Mus; Gerard J den Heeten; Nico Karssemeijer
Journal:  Eur Radiol       Date:  2010-06-09       Impact factor: 5.315

8.  Effects of incorrect computer-aided detection (CAD) output on human decision-making in mammography.

Authors:  Eugenio Alberdi; Andrey Povykalo; Lorenzo Strigini; Peter Ayton
Journal:  Acad Radiol       Date:  2004-08       Impact factor: 3.173

9.  Eye movement statistics in humans are consistent with an optimal search strategy.

Authors:  Jiri Najemnik; Wilson S Geisler
Journal:  J Vis       Date:  2008-03-07       Impact factor: 2.240

Review 10.  Computer-aided detection in breast MRI: a systematic review and meta-analysis.

Authors:  Monique D Dorrius; Marijke C Jansen-van der Weide; Peter M A van Ooijen; Ruud M Pijnappel; Matthijs Oudkerk
Journal:  Eur Radiol       Date:  2011-03-15       Impact factor: 5.315

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  11 in total

1.  Analog Computer-Aided Detection (CAD) information can be more effective than binary marks.

Authors:  Corbin A Cunningham; Trafton Drew; Jeremy M Wolfe
Journal:  Atten Percept Psychophys       Date:  2017-02       Impact factor: 2.199

2.  Is there a safety-net effect with computer-aided detection?

Authors:  Ethan Du-Crow; Susan M Astley; Johan Hulleman
Journal:  J Med Imaging (Bellingham)       Date:  2019-12-26

3.  Advancing Research on Medical Image Perception by Strengthening Multidisciplinary Collaboration.

Authors:  Melissa Treviño; George Birdsong; Ann Carrigan; Peter Choyke; Trafton Drew; Miguel Eckstein; Anna Fernandez; Brandon D Gallas; Maryellen Giger; Stephen M Hewitt; Todd S Horowitz; Yuhong V Jiang; Bonnie Kudrick; Susana Martinez-Conde; Stephen Mitroff; Linda Nebeling; Joseph Saltz; Frank Samuelson; Steven E Seltzer; Behrouz Shabestari; Lalitha Shankar; Eliot Siegel; Mike Tilkin; Jennifer S Trueblood; Alison L Van Dyke; Aradhana M Venkatesan; David Whitney; Jeremy M Wolfe
Journal:  JNCI Cancer Spectr       Date:  2022-01-05

4.  The effect of computer-aided detection markers on visual search and reader performance during concurrent reading of CT colonography.

Authors:  Emma Helbren; Thomas R Fanshawe; Peter Phillips; Susan Mallett; Darren Boone; Alastair Gale; Douglas G Altman; Stuart A Taylor; David Manning; Steve Halligan
Journal:  Eur Radiol       Date:  2015-01-12       Impact factor: 5.315

5.  Quantifying the costs of interruption during diagnostic radiology interpretation using mobile eye-tracking glasses.

Authors:  Trafton Drew; Lauren H Williams; Booth Aldred; Marta E Heilbrun; Satoshi Minoshima
Journal:  J Med Imaging (Bellingham)       Date:  2018-03-02

6.  Eye Tracking for Deep Learning Segmentation Using Convolutional Neural Networks.

Authors:  J N Stember; H Celik; E Krupinski; P D Chang; S Mutasa; B J Wood; A Lignelli; G Moonis; L H Schwartz; S Jambawalikar; U Bagci
Journal:  J Digit Imaging       Date:  2019-08       Impact factor: 4.056

7.  Studying human-AI collaboration protocols: the case of the Kasparov's law in radiological double reading.

Authors:  Federico Cabitza; Andrea Campagner; Luca Maria Sconfienza
Journal:  Health Inf Sci Syst       Date:  2021-02-05

8.  The optimal use of computer aided detection to find low prevalence cancers.

Authors:  Melina A Kunar
Journal:  Cogn Res Princ Implic       Date:  2022-02-04

9.  Simple eye-movement feedback during visual search is not helpful.

Authors:  Trafton Drew; Lauren H Williams
Journal:  Cogn Res Princ Implic       Date:  2017-11-22

10.  Can computer-aided diagnosis assist in the identification of prostate cancer on prostate MRI? a multi-center, multi-reader investigation.

Authors:  Sonia Gaur; Nathan Lay; Stephanie A Harmon; Sreya Doddakashi; Sherif Mehralivand; Burak Argun; Tristan Barrett; Sandra Bednarova; Rossanno Girometti; Ercan Karaarslan; Ali Riza Kural; Aytekin Oto; Andrei S Purysko; Tatjana Antic; Cristina Magi-Galluzzi; Yesim Saglican; Stefano Sioletic; Anne Y Warren; Leonardo Bittencourt; Jurgen J Fütterer; Rajan T Gupta; Ismail Kabakus; Yan Mee Law; Daniel J Margolis; Haytham Shebel; Antonio C Westphalen; Bradford J Wood; Peter A Pinto; Joanna H Shih; Peter L Choyke; Ronald M Summers; Baris Turkbey
Journal:  Oncotarget       Date:  2018-09-18
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