Artificial intelligence - based ultrasound elastography for disease evaluation - a narrative review

Xian-Ya Zhang; Qi Wei; Ge-Ge Wu; Qi Tang; Xiao-Fang Pan; Gong-Quan Chen; Di Zhang; Christoph F Dietrich; Xin-Wu Cui

doi:10.3389/fonc.2023.1197447

Artificial intelligence - based ultrasound elastography for disease evaluation - a narrative review

Front Oncol. 2023 Jun 2:13:1197447. doi: 10.3389/fonc.2023.1197447. eCollection 2023.

Authors

Xian-Ya Zhang¹, Qi Wei¹, Ge-Ge Wu¹, Qi Tang², Xiao-Fang Pan³, Gong-Quan Chen⁴, Di Zhang⁵, Christoph F Dietrich⁶, Xin-Wu Cui¹

Affiliations

¹ Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
² Department of Ultrasonography, The First Hospital of Changsha, Changsha, China.
³ Health Medical Department, Dalian Municipal Central Hospital, Dalian, China.
⁴ Department of Medical Ultrasound, Minda Hospital of Hubei Minzu University, Enshi, China.
⁵ Department of Medical Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
⁶ Department of Internal Medicine, Hirslanden Clinic, Bern, Switzerland.

Abstract

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.

Keywords: artificial intelligence; deep learning; elastography; machine learning; radiomics; ultrasound.

Publication types

Review

Grants and funding

This work was supported by the National Natural Science Foundation of China [grant numbers 82071953] of X-WC, who have helped to design the study and decide to submit the article for publication.