Use of Artificial Intelligence for Reducing Unnecessary Recalls at Screening Mammography: A Simulation Study
Yeon Soo Kim, Myoung‐jin Jang, Su Hyun Lee, Soo‐Yeon Kim, Su Min Ha, Bo Ra Kwon, Woo Kyung Moon, Jung Min Chang
Abstract
OBJECTIVE: To conduct a simulation study to determine whether artificial intelligence (AI)-aided mammography reading can reduce unnecessary recalls while maintaining cancer detection ability in women recalled after mammography screening. MATERIALS AND METHODS: A retrospective reader study was performed by screening mammographies of 793 women (mean age ± standard deviation, 50 ± 9 years) recalled to obtain supplemental mammographic views regarding screening mammography-detected abnormalities between January 2016 and December 2019 at two screening centers. Initial screening mammography examinations were interpreted by three dedicated breast radiologists sequentially, case by case, with and without AI aid, in a single session. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and recall rate for breast cancer diagnosis were obtained and compared between the two reading modes. RESULTS: < 0.001). CONCLUSION: AI-aided reading reduced the number of recalls and improved the diagnostic performance in our simulation using women initially recalled for supplemental mammographic views after mammography screening.