Literature DB >> 20679448

Interpretation time of computer-aided detection at screening mammography.

Philip M Tchou1, Tamara Miner Haygood, E Neely Atkinson, Tanya W Stephens, Paul L Davis, Elsa M Arribas, William R Geiser, Gary J Whitman.   

Abstract

PURPOSE: To prospectively determine the interpretation time associated with computer-aided detection (CAD) and to analyze how CAD affected radiologists' decisions and their level of confidence in their interpretations of digital screening mammograms.
MATERIALS AND METHODS: An Institutional Review Board exemption was obtained, and patient consent was waived in this HIPAA compliant study. The participating radiologists gave informed consent. Five radiologists were prospectively studied as they interpreted 267 clinical digital screening mammograms. Interpretation times, recall decisions, and confidence levels were recorded without CAD and then with CAD. Software was used for linear regression fitting of interpretation times. P values less than .05 were considered to indicate statistically significant differences.
RESULTS: Mean interpretation time without CAD was 118 seconds ± 4.2 (standard error of the mean). Mean time for reviewing CAD images was 23 seconds ± 1.5. CAD identified additional findings in five cases, increased confidence in 38 cases, and decreased confidence in 21 cases. Interpretation time without CAD increased with the number of mammographic views (P < .0001). Mean times for interpretation without CAD and review of the CAD images both increased with the number of CAD marks (P < .0001). The interpreting radiologist was a significant variable for all interpretation times (P < .0001). Interpretation time with CAD increased by 3.2 seconds (95% confidence interval: 1.8, 4.6) for each calcification cluster marked and by 7.3 seconds (95% confidence interval: 4.7, 9.9) for each mass marked.
CONCLUSION: The additional time required to review CAD images represented a 19% increase in the mean interpretation time without CAD. CAD requires a considerable time investment for digital screening mammography but may provide less measureable benefits in terms of confidence of the radiologists.

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Year:  2010        PMID: 20679448     DOI: 10.1148/radiol.10092170

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  11 in total

1.  Is confidence of mammographic assessment a good predictor of accuracy?

Authors:  Berta M Geller; Andy Bogart; Patricia A Carney; Joann G Elmore; Barbara S Monsees; Diana L Miglioretti
Journal:  AJR Am J Roentgenol       Date:  2012-07       Impact factor: 3.959

2.  The Rebirth of CAD: How Is Modern AI Different from the CAD We Know?

Authors:  Luke Oakden-Rayner
Journal:  Radiol Artif Intell       Date:  2019-05-29

3.  Impact of an abbreviated protocol for breast MRI in diagnostic accuracy.

Authors:  Guillaume Oldrini; Imad Derraz; Julia Salleron; Frédéric Marchal; Philippe Henrot
Journal:  Diagn Interv Radiol       Date:  2018 Jan-Feb       Impact factor: 2.630

4.  Association between time spent interpreting, level of confidence, and accuracy of screening mammography.

Authors:  Patricia A Carney; T Andrew Bogart; Berta M Geller; Sebastian Haneuse; Karla Kerlikowske; Diana S M Buist; Robert Smith; Robert Rosenberg; Bonnie C Yankaskas; Tracy Onega; Diana L Miglioretti
Journal:  AJR Am J Roentgenol       Date:  2012-04       Impact factor: 3.959

5.  Artificial Intelligence in Imaging: The Radiologist's Role.

Authors:  Daniel L Rubin
Journal:  J Am Coll Radiol       Date:  2019-09       Impact factor: 5.532

6.  Improving Accuracy and Efficiency with Concurrent Use of Artificial Intelligence for Digital Breast Tomosynthesis.

Authors:  Emily F Conant; Alicia Y Toledano; Senthil Periaswamy; Sergei V Fotin; Jonathan Go; Justin E Boatsman; Jeffrey W Hoffmeister
Journal:  Radiol Artif Intell       Date:  2019-07-31

7.  Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach.

Authors:  Abdul Rahman Diab; Bryan Haslam; Jiye G Kim; William Lotter; Giorgia Grisot; Eric Wu; Kevin Wu; Jorge Onieva Onieva; Yun Boyer; Jerrold L Boxerman; Meiyun Wang; Mack Bandler; Gopal R Vijayaraghavan; A Gregory Sorensen
Journal:  Nat Med       Date:  2021-01-11       Impact factor: 87.241

Review 8.  Abbreviated Magnetic Resonance Imaging for Breast Cancer Screening: Concept, Early Results, and Considerations.

Authors:  Eun Sook Ko; Elizabeth A Morris
Journal:  Korean J Radiol       Date:  2019-04       Impact factor: 3.500

9.  Reduction of False-Positive Markings on Mammograms: a Retrospective Comparison Study Using an Artificial Intelligence-Based CAD.

Authors:  Ray Cody Mayo; Daniel Kent; Lauren Chang Sen; Megha Kapoor; Jessica W T Leung; Alyssa T Watanabe
Journal:  J Digit Imaging       Date:  2019-08       Impact factor: 4.056

10.  Interpretation time for screening mammography as a function of the number of computer-aided detection marks.

Authors:  Tayler M Schwartz; Stephen L Hillis; Radhika Sridharan; Olga Lukyanchenko; William Geiser; Gary J Whitman; Wei Wei; Tamara Miner Haygood
Journal:  J Med Imaging (Bellingham)       Date:  2020-02-03
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