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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/159570


    Title: Recognizing artery segments on carotid ultrasonography using embedding concatenation of deep image and vision-language models
    Authors: 羅崇銘
    Lo, Chung-Ming;Sung, Sheng-Feng
    Contributors: 圖檔所
    Keywords: artificial intelligence;convolutional neural network;neck ultrasound;vision transformer;vision-language model
    Date: 2025-05
    Issue Date: 2025-09-24 09:29:11 (UTC+8)
    Abstract: Objective.Evaluating large artery atherosclerosis is critical for predicting and preventing ischemic strokes. Ultrasonographic assessment of the carotid arteries is the preferred first-line examination due to its ease of use, noninvasive, and absence of radiation exposure. This study proposed an automated classification model for the common carotid artery (CCA), carotid bulb, internal carotid artery (ICA), and external carotid artery (ECA) to enhance the quantification of carotid artery examinations.Approach. A total of 2943 B-mode ultrasound images (CCA: 1563; bulb: 611; ICA: 476; ECA: 293) from 288 patients were collected. Three distinct sets of embedding features were extracted from artificial intelligence networks including pre-trained DenseNet201, vision transformer, and echo contrastive language-image pre-training models using deep learning architectures for pattern recognition. These features were then combined in a support vector machine classifier to interpret the anatomical structures in B-mode images.Main results. After ten-fold cross-validation, the model achieved an accuracy of 82.3%, which was significantly better than using individual feature sets, with ap-value of <0.001.Significance.The proposed model could make carotid artery examinations more accurate and consistent with the achieved classification accuracy. The source code is available athttps://github.com/buddykeywordw/Artery-Segments-Recognition.
    Relation: Physics in Medicine & Biology, Vol.70, No.11, 115008
    Data Type: article
    DOI 連結: https://doi.org/10.1088/1361-6560/add8db
    DOI: 10.1088/1361-6560/add8db
    Appears in Collections:[圖書資訊與檔案學研究所] 期刊論文

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