AI-Driven quality assurance in mammography: Enhancing quality control efficiency through automated phantom image evaluation in South Korea.

ABSTRACT:

Purpose

To develop and validate a deep learning-based model for automated evaluation of mammography phantom images, with the goal of improving inter-radiologist agreement and enhancing the efficiency of quality control within South Korea's national accreditation system.

Materials and methods

A total of 5,917 mammography phantom images were collected from the Korea Institute for Accreditation of Medical Imaging (KIAMI). After preprocessing, 5,813 images (98.2%) met quality standards and were divided into training, test, and evaluation datasets. Each image included 16 artificial lesions (fibers, specks, masses) scored by certified radiologists. Images were preprocessed, standardized, and divided into 16 subimages. An EfficientNetV2_L-based model, selected for its balance of ac

SUBMITTER: Yun H 

PROVIDER: S-EPMC12416691 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature