Literature DB >> 2394571

Computer-displayed eye position as a visual aid to pulmonary nodule interpretation.

H L Kundel1, C F Nodine, E A Krupinski.   

Abstract

Approximately 30% of nodules are missed during the initial reading of chest radiographs. Eye-position recordings have shown that most nodules that are missed receive prolonged visual attention. A computer algorithm was developed that uses eye-position and gaze-duration times to identify locations on the chest image likely to contain missed nodules. These locations are highlighted on the displayed image to give visual feedback. The current study tested whether visual feedback was an effective aid to nodule detection. Six radiology residents searched 40 chest images for nodules while their eye-position and gaze-duration times were recorded. Half received displayed visual feedback and half were given a second view without feedback. Two months later the two groups returned and viewed the images in the opposite condition to counterbalance for possible practice effects. Performance of readers who were given feedback showed an average of 16% improvement as measured by the alternative free response operating characteristic (AFROC) curve area, A1. Performance of the same readers given a second look without feedback did not improve.

Entities:  

Mesh:

Year:  1990        PMID: 2394571     DOI: 10.1097/00004424-199008000-00004

Source DB:  PubMed          Journal:  Invest Radiol        ISSN: 0020-9996            Impact factor:   6.016


  14 in total

1.  Collaborative eye tracking: a potential training tool in laparoscopic surgery.

Authors:  Andrew S A Chetwood; Ka-Wai Kwok; Loi-Wah Sun; George P Mylonas; James Clark; Ara Darzi; Guang-Zhong Yang
Journal:  Surg Endosc       Date:  2012-01-19       Impact factor: 4.584

2.  Investigating the link between radiologists' gaze, diagnostic decision, and image content.

Authors:  Georgia Tourassi; Sophie Voisin; Vincent Paquit; Elizabeth Krupinski
Journal:  J Am Med Inform Assoc       Date:  2013-06-20       Impact factor: 4.497

Review 3.  Emerging applications of eye-tracking technology in dermatology.

Authors:  Kevin K John; Jakob D Jensen; Andy J King; Manusheela Pokharel; Douglas Grossman
Journal:  J Dermatol Sci       Date:  2018-04-06       Impact factor: 4.563

4.  Sparseness of the trabecular pattern on dental radiographs: visual assessment compared with semi-automated measurements.

Authors:  W G M Geraets; C Lindh; H Verheij
Journal:  Br J Radiol       Date:  2012-02-28       Impact factor: 3.039

Review 5.  Review of prospects and challenges of eye tracking in volumetric imaging.

Authors:  Antje C Venjakob; Claudia R Mello-Thoms
Journal:  J Med Imaging (Bellingham)       Date:  2015-09-29

6.  Perceptual enhancement of tumor targets in chest X-ray images.

Authors:  E A Krupinski; C F Nodine; H L Kundel
Journal:  Percept Psychophys       Date:  1993-05

7.  Application of threshold-bias independent analysis to eye-tracking and FROC data.

Authors:  Dev P Chakraborty; Hong-Jun Yoon; Claudia Mello-Thoms
Journal:  Acad Radiol       Date:  2012-10-04       Impact factor: 3.173

8.  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

9.  Using Highlighting to Train Attentional Expertise.

Authors:  Brett Roads; Michael C Mozer; Thomas A Busey
Journal:  PLoS One       Date:  2016-01-08       Impact factor: 3.240

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

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