A machine learning model based on readers' characteristics to predict their performances in reading screening mammograms.

ABSTRACT:

Objectives

Proposing a machine learning model to predict readers' performances, as measured by the area under the receiver operating characteristics curve (AUC) and lesion sensitivity, using the readers' characteristics.

Methods

Data were collected from 905 radiologists and breast physicians who completed at least one case-set of 60 mammographic images containing 40 normal and 20 biopsy-proven cancer cases. Nine different case-sets were available. Using a questionnaire, we collected radiologists' demographic details, such as reading volume and years of experience. These characteristics along with a case set difficulty measure were fed into two ensemble of regression trees to predict the readers' AUCs and lesion sensitivities. We calculated the Pearson correlation coefficient

SUBMITTER: Gandomkar Z 

PROVIDER: S-EPMC9226081 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature