Performance of Digital Mammography-Based Artificial Intelligence Computer-Aided Diagnosis on Synthetic Mammography From Digital Breast Tomosynthesis.

Journal: Korean journal of radiology

PMID: 39999963

Abstract

OBJECTIVE: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for full-field digital mammography (FFDM) when applied to synthetic mammography (SM).

Authors

Kyung Eun Lee

College of Pharmacy, Chungbuk National University, 660-1 Yeonje-ri, Osong-eup, Heungdeok-gu, Cheongju, 28160, Republic of Korea. kaylee@cbnu.ac.kr.
Sung Eun Song

Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
Kyu Ran Cho

Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73, Goryeodae-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea.
Min Sun Bae

Department of Radiology, Seoul National University Hospital, Seoul 110-744, South Korea.
Bo Kyoung Seo
Soo-Yeon Kim
Ok Hee Woo

Department of Radiology, Korea University Guro Hospital, Seoul, Korea.

Keywords

Adult Aged Artificial Intelligence Breast Breast Neoplasms Diagnosis, Computer-Assisted Female Humans Mammography Middle Aged Radiographic Image Interpretation, Computer-Assisted Retrospective Studies Sensitivity and Specificity

External Resources

View on PubMed Access via DOI PubMed (39999963)

Performance of Digital Mammography-Based Artificial Intelligence Computer-Aided Diagnosis on Synthetic Mammography From Digital Breast Tomosynthesis.

Abstract

Authors

Keywords

External Resources

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