Differing benefits of artificial intelligence-based computer-aided diagnosis for breast US according to workflow and experience level.

ABSTRACT:

Purpose

This study evaluated how artificial intelligence-based computer-assisted diagnosis (AICAD) for breast ultrasonography (US) influences diagnostic performance and agreement between radiologists with varying experience levels in different workflows.

Methods

Images of 492 breast lesions (200 malignant and 292 benign masses) in 472 women taken from April 2017 to June 2018 were included. Six radiologists (three inexperienced [<1 year of experience] and three experienced [10-15 years of experience]) individually reviewed US images with and without the aid of AI-CAD, first sequentially and then simultaneously. Diagnostic performance and interobserver agreement were calculated and compared between radiologists and AI-CAD.

Results

After implementing AI-CAD, the specific

SUBMITTER: Lee SE 

PROVIDER: S-EPMC9532201 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature