Kim Jaeil, Kim Hye Jung, Kim Chanho, Kim Won Hwa
School of Computer Science and Engineering, Kyungpook National University, Daegu, Korea.
Department of Radiology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu, Korea.
Ultrasonography. 2021 Apr;40(2):183-190. doi: 10.14366/usg.20117. Epub 2020 Nov 12.
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.
尽管乳腺超声检查是鉴别乳腺良恶性肿块的主要手段，但它存在假阳性和观察者间显著差异等固有问题。人工智能（AI），尤其是深度学习模型，有望提高工作流程效率并提供辅助诊断意见。AI在乳腺超声检查中执行三项主要临床任务时非常有用：检测（定位/分割）、鉴别诊断（分类）和预后评估（预测）。本文概述了AI在乳腺超声检查中的应用现状，讨论了AI模型开发中的方法学考量，并对潜在临床应用进行了最新文献综述。
