Artificial intelligence in breast ultrasonography
Ultrasonography 2021³â 40±Ç 2È£ p.183 ~ p.190
±èÀçÀÏ(Kim Jae-Il) - Kyungpook National University School of Computer Science and Engineering
±èÇýÁ¤(Kim Hye-Jung) - Kyungpook National University School of Medicine Kyungpook National University Chilgok Hospital Department of Radiology
±èÂùÈ£(Kim Chan-Ho) - Kyungpook National University School of Computer Science and Engineering
±è¿øÈ(Kim Won-Hwa) - Kyungpook National University School of Medicine Kyungpook National University Chilgok Hospital Department of Radiology
Abstract
Although breast ultrasonography is the mainstay modality for differentiating between benign and malignant breast masses, it has intrinsic problems with false positives and substantial interobserver variability. Artificial intelligence (AI), particularly with deep learning models, is expected to improve workflow efficiency and serve as a second opinion. AI is highly useful for performing three main clinical tasks in breast ultrasonography: detection (localization/segmentation), differential diagnosis (classification), and prognostication (prediction). This article provides a current overview of AI applications in breast ultrasonography, with a discussion of methodological considerations in the development of AI models and an up-to-date literature review of potential clinical applications.
Å°¿öµå
Artificial intelligence, Breast neoplasm, Ultrasonography, Convolutional neural network, Breast diseases
¿ø¹® ¹× ¸µÅ©¾Æ¿ô Á¤º¸
µîÀçÀú³Î Á¤º¸
À¯È¿¼º°á°ú(Recomendation)
This article provides a current overview of AI applications in breast ultrasonography, with a discussion of methodological considerations in the development of AI models and an up-to-date literature review of potential clinical applications.