Volume 24 Issue 4
Apr.  2026
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Article Navigation >  Chinese Journal of General Practice  > 2026  >  24(4): 633-637
LIU Wenli, ZHANG Shuhua, WANG Yihua, LIU Yang, ZHANG Xue, YAN Huishan, LIN Hongzhao, YIN Tong, LI Ning. Visual analysis of research progress of artificial intelligence in carotid plaque ultrasound examination[J]. Chinese Journal of General Practice, 2026, 24(4): 633-637. doi: 10.16766/j.cnki.issn.1674-4152.004457
Citation: LIU Wenli, ZHANG Shuhua, WANG Yihua, LIU Yang, ZHANG Xue, YAN Huishan, LIN Hongzhao, YIN Tong, LI Ning. Visual analysis of research progress of artificial intelligence in carotid plaque ultrasound examination[J]. Chinese Journal of General Practice, 2026, 24(4): 633-637. doi: 10.16766/j.cnki.issn.1674-4152.004457
shu

Visual analysis of research progress of artificial intelligence in carotid plaque ultrasound examination

doi: 10.16766/j.cnki.issn.1674-4152.004457

  • Department of Ultrasound, North China University of Science and Technology Affiliated Hospital, Tangshan, Hebei 063000, China

Funds:

 ZF2025282

  • Received Date: 2025-01-24
    Abstract
  •   Objective  To provide a reference direction for the research on the application of artificial intelligence (AI) in the recognition and analysis of carotid plaque ultrasound images by analyzing the research status and progress of AI in carotid plaque ultrasound at home and abroad.  Methods  Using the China National Knowledge Infrastructure (CNKI), Wanfang Data, Web of Science, PubMed, and IEEE as databases, relevant research literatures on the application of AI in carotid plaque ultrasound from the establishment of the databases to December 2024 were retrieved as the research objects, and CiteSpace was used for visual analysis.  Results  After retrieval and screening, a total of 14 Chinese-language references and 88 foreign-language references were ultimately included. The number of literatures published at home and abroad on the application of AI in carotid plaque ultrasound is relatively small, and the publishing trends are both tending to be flat. There is cooperation among relevant researchers and institutions, but the degree of close cooperation is low. Keywords with high frequencies of occurrence include deep learning, machine learning, plaque segmentation, stroke, convolutional neural network, carotid plaque characterization, etc.  Conclusion  The research hotspots of AI in carotid plaque ultrasound at home and abroad mainly focus on the application of deep learning in identifying carotid plaques in ultrasound images, AI - based plaque segmentation technology, and the relationship between AI - analyzed ultrasound characteristics of carotid plaques and the occurrence of stroke and cardiovascular diseases. Computer-aided diagnosis and classification algorithms will become the research trend of AI in carotid plaque ultrasound. Meanwhile, due to the limited research on the application of AI in carotid plaque ultrasound examination at present, its application value in clinical practice remains to be verified and improved. In the future, more researchers are needed to engage in research on the application of AI in carotid plaque ultrasound examination.

     

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