Predicting false negative errors in digital breast tomosynthesis among radiology trainees using a computer vision-based approach

作者：

Highlights：

• We developed a model to predict whether a lesion will be missed by a trainee.

• The user model can be used to select the most challenging cases for each trainee.

• Our model improved the status quo of case presentation to trainee in tomosynthesis.

摘要

•We developed a model to predict whether a lesion will be missed by a trainee.•The user model can be used to select the most challenging cases for each trainee.•Our model improved the status quo of case presentation to trainee in tomosynthesis.

论文关键词：Radiology education,False negative error prediction,Training protocol optimization,Tomosynthesis

论文评审过程：Received 29 November 2015, Revised 22 January 2016, Accepted 25 January 2016, Available online 12 February 2016, Version of Record 16 March 2016.

论文官网地址：https://doi.org/10.1016/j.eswa.2016.01.053