Predicting false negative errors in digital breast tomosynthesis among radiology trainees using a computer vision-based approach
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- 作者：Mengyu Wanga ; b ; mengyu_wang@meei.harvard.edu" class="auth_mail" title="E-mail the corresponding author ; wmy32552316wmy@gmail.com" class="auth_mail" title="E-mail the corresponding author ; Jing Zhanga ; c ; jing.zhang@lamar.edu" class="auth_mail" title="E-mail the corresponding author ; Lars J. Grimma ; lars.grimm@duke.edu" class="auth_mail" title="E-mail the corresponding author ; Sujata V. Ghatea ; sujata.ghate@duke.edu" class="auth_mail" title="E-mail the corresponding author ; Ruth Walsha ; ruth.walsh@duke.edu" class="auth_mail" title="E-mail the corresponding author ; Karen S. Johnsona ; karen.johnson2@duke.edu" class="auth_mail" title="E-mail the corresponding author ; Joseph Y. Loa ; joseph.lo@duke.edu" class="auth_mail" title="E-mail the corresponding author ; Maciej A. Mazurowskia ; maciej.mazurowski@duke.edu" class="auth_mail" title="E-mail the corresponding author
- 关键词：Radiology education ; False negative error prediction ; Training protocol optimization ; Tomosynthesis
- 刊名：Expert Systems with Applications
- 出版年：2016
- 出版时间：1 September 2016
- 年：2016
- 卷：56
- 期：Complete
- 页码：1-8
- 全文大小：1840 K
文摘
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We developed a model to predict whether a lesion will be missed by a trainee.
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The user model can be used to select the most challenging cases for each trainee.
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Our model improved the status quo of case presentation to trainee in tomosynthesis.