Application and prospects of AI-based radiomics in ultrasound diagnosis

被引:0
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作者
Haoyan Zhang
Zheling Meng
Jinyu Ru
Yaqing Meng
Kun Wang
机构
[1] Chinese Academy of Sciences,CAS Key Laboratory of Molecular Imaging, Institute of Automation
[2] University of Chinese Academy of Sciences,School of Artificial Intelligence
关键词
Radiomics; Ultrasound imaging; Artificial intelligence; Deep learning; B-mode ultrasound; Color Doppler flow imaging; Ultrasound elastography; Contrast-enhanced ultrasound; Multimodal ultrasound;
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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.
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