Modeling Radiologists' Assessments to Explore Pairing Strategies for Optimized Double Reading of Screening Mammograms.

Jessie J J GommersCraig K AbbeyFredrik StrandSian Taylor-PhillipsDavid J JenkinsonMarthe LarsenSolveig HofvindMireille J M BroedersIoannis Sechopoulos

Published in: Medical decision making : an international journal of the Society for Medical Decision Making (2024)

A logistic-regression model can be derived that accurately predicts individual and paired reader performance during mammography screening reading.Pairing screening mammography radiologists with similar false-positive characteristics reduced false-positive rates with no significant loss in true positives and may reduce the number of examinations unnecessarily sent to consensus/arbitration.

Keyphrases

artificial intelligence
working memory
contrast enhanced
magnetic resonance imaging
image quality
machine learning
magnetic resonance
deep learning