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2000
Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
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Abstract

Background and Objective:

The incidence of stroke is rising, and it is the second major cause of mortality and the third leading cause of disability globally. The goal of this study was to rapidly and accurately identify carotid plaques and automatically quantify plaque burden using our automated tracking and segmentation US-video system.

Methods:

We collected 88 common carotid artery transection videos (11048 frames) with a history of atherosclerosis or risk factors for atherosclerosis, which were randomly divided into training, test, and validation sets using a 6:3:1 ratio. We first trained different segmentation models to segment the carotid intima and adventitia, and calculate the maximum plaque burden automatically. Finally, we statistically analyzed the plaque burden calculated automatically by the best model and the results of manual labeling by senior sonographers.

Results:

Of the three Artificial Intelligence (AI) models, the Robust Video Matting (RVM) segmentation model's carotid intima and adventitia Dice Coefficients (DC) were the highest, reaching 0.93 and 0.95, respectively. Moreover, the RVM model has shown the strongest correlation coefficient (0.61±0.28) with senior sonographers, and the diagnostic effectiveness between the RVM model and experts was comparable with paired-t test and Bland-Altman analysis [P= 0.632 and ICC 0.01 (95% CI: -0.24~0.27), respectively].

Conclusion:

Our findings have indicated that the RVM model can be used in ultrasound carotid video. The RVM model can automatically segment and quantify atherosclerotic plaque burden at the same diagnostic level as senior sonographers. The application of AI to carotid videos offers more precise and effective methods to evaluate carotid atherosclerosis in clinical practice.

© 2024 The Author(s). Published by Bentham Science Publisher.
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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2024-04-18
2025-07-26

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A Video-based Automated Tracking and Analysis System of Plaque Burden in Carotid Artery using Deep Learning: A Comparison with Senior Sonographers
Curr. Med. Imaging 20, e15734056296233 (2024); https://doi.org/10.2174/0115734056296233240401061756
/content/journals/cmir/10.2174/0115734056296233240401061756
/content/journals/cmir/10.2174/0115734056296233240401061756
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  • Article Type:     Research Article
Keyword(s): Arteriosclerosis; Artificial intelligence; Carotid artery video; Deep learning; Maximum plaque burden; Sonographers

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