Artificial intelligence in breast ultrasonography

• Published in: Ultrasonography (Seoul, Korea) Vol. 40; no. 2; pp. 183 - 190
• Main Authors: Kim, Jaeil, Kim, Hye Jung, Kim, Chanho, Kim, Won Hwa
• Format: Journal Article
• Language: English
• Published: Korea (South) Korean Society of Ultrasound in Medicine 01.04.2021; 대한초음파의학회
• Subjects: Algorithms; Annotations; Artificial intelligence; Automation; Breast cancer; breast diseases; breast neoplasm; Classification; convolutional neural network; Datasets; Decision making; Feasibility studies; Localization; Mammography; Medical imaging; Neural networks; Special Review of Artificial Intelligence (part 2); Ultrasonic imaging; ultrasonography; Womens health; Workloads; 방사선과학; Breast diseases; Artificial intelligence; Convolutional neural network; Ultrasonography; Breast neoplasm
• ISSN: 2288-5919; 2288-5943
• DOI: 10.14366/usg.20117

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.