Advances in the Application of Artificial Intelligence in the Ultrasound Diagnosis of Vulnerable Carotid Atherosclerotic Plaque.
Saved in:
| Title: | Advances in the Application of Artificial Intelligence in the Ultrasound Diagnosis of Vulnerable Carotid Atherosclerotic Plaque. |
|---|---|
| Authors: | Wang, Dan-dan1 (AUTHOR), Lin, Shu2,3 (AUTHOR), Lyu, Guo-rong1,4 (AUTHOR) lgr_feus@sina.com |
| Source: | Ultrasound in Medicine & Biology. Apr2025, Vol. 51 Issue 4, p607-614. 8p. |
| Subjects: | Carotid artery ultrasonography, Diagnostic ultrasonic imaging, Atherosclerotic plaque, Ultrasonic imaging, Artificial intelligence |
| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Ultrasound in Medicine & Biology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
Be the first to leave a comment!
