Zhang Xian-Ya, Wei Qi, Wu Ge-Ge, Tang Qi, Pan Xiao-Fang, Chen Gong-Quan, Zhang Di, Dietrich Christoph F, Cui Xin-Wu
Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Department of Ultrasonography, The First Hospital of Changsha, Changsha, China.
Front Oncol. 2023 Jun 2;13:1197447. doi: 10.3389/fonc.2023.1197447. eCollection 2023.
Ultrasound elastography (USE) provides complementary information of tissue stiffness and elasticity to conventional ultrasound imaging. It is noninvasive and free of radiation, and has become a valuable tool to improve diagnostic performance with conventional ultrasound imaging. However, the diagnostic accuracy will be reduced due to high operator-dependence and intra- and inter-observer variability in visual observations of radiologists. Artificial intelligence (AI) has great potential to perform automatic medical image analysis tasks to provide a more objective, accurate and intelligent diagnosis. More recently, the enhanced diagnostic performance of AI applied to USE have been demonstrated for various disease evaluations. This review provides an overview of the basic concepts of USE and AI techniques for clinical radiologists and then introduces the applications of AI in USE imaging that focus on the following anatomical sites: liver, breast, thyroid and other organs for lesion detection and segmentation, machine learning (ML) - assisted classification and prognosis prediction. In addition, the existing challenges and future trends of AI in USE are also discussed.
超声弹性成像（USE）为传统超声成像提供了组织硬度和弹性的补充信息。它是非侵入性的且无辐射，已成为提高传统超声成像诊断性能的有价值工具。然而，由于放射科医生在视觉观察中的高度操作者依赖性以及观察者内和观察者间的变异性，诊断准确性会降低。人工智能（AI）在执行自动医学图像分析任务以提供更客观、准确和智能的诊断方面具有巨大潜力。最近，已证明AI应用于USE在各种疾病评估中具有增强的诊断性能。本综述为临床放射科医生提供了USE和AI技术的基本概念概述，然后介绍了AI在USE成像中的应用，重点关注以下解剖部位：肝脏、乳腺、甲状腺和其他器官的病变检测与分割、机器学习（ML）辅助分类和预后预测。此外，还讨论了AI在USE中存在的挑战和未来趋势。
