Application and prospects of AI-based radiomics in ultrasound diagnosis

作     者：Haoyan Zhang Zheling Meng Jinyu Ru Yaqing Meng Kun Wang 

作者机构：CAS Key Laboratory of Molecular ImagingInstitute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijing 100190China 

出 版 物：《Visual Computing for Industry,Biomedicine,and Art》 (工医艺的可视计算（英文）)

年 卷 期：2023年第6卷第1期

页      面：288-303页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 100207[医学-影像医学与核医学] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 1002[医学-临床医学] 081104[工学-模式识别与智能系统] 08[工学] 1010[医学-医学技术（可授医学、理学学位）] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

基　　金：the National Natural Science Foundation of China,Nos.92159305,92259303,62027901,81930053,and 82272029 Beijing Science Fund for Distinguished Young Scholars,No.JQ22013 and Excellent Member Project of the Youth Innovation Promotion Association CAS,No.2016124 

主　　题：Radiomics Ultrasound imaging Artificial intelligence Deep learning B-mode ultrasound Color Doppler flow imaging Ultrasound elastography Contrast-enhanced ultrasound Multimodal ultrasound 

摘      要：Artificial intelligence (AI)-based radiomics has attracted considerable research attention in the field of medical imaging, including ultrasound diagnosis. Ultrasound imaging has unique advantages such as high temporal resolution, low cost, and no radiation exposure. This renders it a preferred imaging modality for several clinical scenarios. This review includes a detailed introduction to imaging modalities, including Brightness-mode ultrasound, color Doppler flow imaging, ultrasound elastography, contrast-enhanced ultrasound, and multi-modal fusion analysis. It provides an overview of the current status and prospects of AI-based radiomics in ultrasound diagnosis, highlighting the application of AI-based radiomics to static ultrasound images, dynamic ultrasound videos, and multi-modal ultrasound fusion analysis.