External Validation of an Ensemble Model for Automated Mammography Interpretation by Artificial Intelligence.

ABSTRACT:

Importance

With a shortfall in fellowship-trained breast radiologists, mammography screening programs are looking toward artificial intelligence (AI) to increase efficiency and diagnostic accuracy. External validation studies provide an initial assessment of how promising AI algorithms perform in different practice settings.

Objective

To externally validate an ensemble deep-learning model using data from a high-volume, distributed screening program of an academic health system with a diverse patient population.

Design, setting, and participants

In this diagnostic study, an ensemble learning method, which reweights outputs of the 11 highest-performing individual AI models from the Digital Mammography Dialogue on Reverse Engineering Assessment and Methods (DREAM) Mammog

SUBMITTER: Hsu W 

PROVIDER: S-EPMC9679879 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature