Advances in the Application of Artificial Intelligence in the Ultrasound Diagnosis of Vulnerable Carotid Atherosclerotic Plaque

被引:0
作者
Wang, Dan-dan [1 ]
Lin, Shu [2 ,3 ]
Lyu, Guo-rong [1 ,4 ]
机构
[1] Fujian Med Univ, Affiliated Hosp 2, Dept Ultrasound, 34 North Zhongshan Rd, Quanzhou 362000, Fujian, Peoples R China
[2] Fujian Med Univ, Affiliated Hosp 2, Ctr Neurol & Metab Res, Quanzhou, Peoples R China
[3] Garvan Inst Med Res, Grp Neuroendocrinol, Sydney, Australia
[4] Quanzhou Med Coll, Dept Med Imaging, Quanzhou, Peoples R China
关键词
Artificial intelligence; Ultrasound; Carotid plaques; Vulnerable; Atherosclerosis; CONTRAST-ENHANCED ULTRASOUND; RISK STRATIFICATION; B-MODE; TISSUE CHARACTERIZATION; NEURAL-NETWORKS; SEGMENTATION; IMAGES; VALIDATION; PATIENT; ARTERY;
D O I
10.1016/j.ultrasmedbio.2024.12.010
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
Vulnerable atherosclerotic plaque is a type of plaque that poses a significant risk of high mortality in patients with cardiovascular disease. Ultrasound has long been used for carotid atherosclerosis screening and plaque assessment due to its safety, low cost and non-invasive nature. However, conventional ultrasound techniques have limitations such as subjectivity, operator dependence, and low inter-observer agreement, leading to inconsistent and possibly inaccurate diagnoses. In recent years, a promising approach to address these limitations has emerged through the integration of artificial intelligence (AI) into ultrasound imaging. It was found that by training AI algorithms with large data sets of ultrasound images, the technology can learn to recognize specific characteristics and patterns associated with vulnerable plaques. This allows for a more objective and consistent assessment, leading to improved diagnostic accuracy. This article reviews the application of AI in the field of diagnostic ultrasound, with a particular focus on carotid vulnerable plaques, and discusses the limitations and prospects of AI-assisted ultrasound. This review also provides a deeper understanding of the role of AI in diagnostic ultrasound and promotes more research in the field.
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页码:607 / 614
页数:8
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