À±Á¤Çö(Yoon Jung-Hyun) - Severance Hospital Department of Radiology
±èÀº°æ(Kim Eun-Kyung) - Yonsei University College of Medicine Yongin Severance Hospital Department of Radiology
Abstract
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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Breast cancer, Mammography, Computer-aided diagnosis, Artificial intelligence, Deep learning
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À¯È¿¼º°á°ú(Recomendation)
Recent advances in technology have enabled the application of AI to mammography, with stand-alone diagnostic performances comparable to those of radiologists, improvements in sensitivity or specificity in breast cancer diagnosis, and the potential reduction in the workflow or interpretation time.