Korean Journal of Radiology

  • 제22권8호
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  • Pages.1225-1239
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  • 2021
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  • 1229-6929(pISSN)
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  • 2005-8330(eISSN)
대한영상의학회 (The Korean Society of Radiology)
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Deep Learning-Based Artificial Intelligence for Mammography

  • Jung Hyun Yoon (Department of Radiology, Severance Hospital, Research Institute of Radiological Science) ;
  • Eun-Kyung Kim (Department of Radiology, Yongin Severance Hospital, Yonsei University, College of Medicine)
  • 투고 : 2020.10.05
  • 심사 : 2021.01.17
  • 발행 : 2021.08.01
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초록

During the past decade, researchers have investigated the use of computer-aided mammography interpretation. With the application of deep learning technology, artificial intelligence (AI)-based algorithms for mammography have shown promising results in the quantitative assessment of parenchymal density, detection and diagnosis of breast cancer, and prediction of breast cancer risk, enabling more precise patient management. AI-based algorithms may also enhance the efficiency of the interpretation workflow by reducing both the workload and interpretation time. However, more in-depth investigation is required to conclusively prove the effectiveness of AI-based algorithms. This review article discusses how AI algorithms can be applied to mammography interpretation as well as the current challenges in its implementation in real-world practice.

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과제정보

The authors thank Medical Illustration & Design, part of the Medical Research Support Services of Yonsei University College of Medicine, for artistic support related to this work.

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