Key result
An automated classification model using concatenated embeddings achieved an accuracy of 82.3% for recognizing carotid artery segments, significantly outperforming individual feature sets (p<0.001).
Why the study?
Evaluating large artery atherosclerosis is critical for predicting and preventing ischemic strokes, motivating an automated classification model of carotid artery segments to enhance examination quantification.
Does an automated classification model using embedding concatenation of deep image and vision-language models improve the accuracy of recognizing artery segments on carotid ultrasonography compared to individual feature sets?
Does an automated classification model using embedding concatenation of deep image and vision-language models improve the accuracy of recognizing artery segments on carotid ultrasonography compared to individual feature sets?
An automated classification model combining deep image and vision-language models achieved 82.3% accuracy in recognizing carotid artery segments on ultrasound, potentially enhancing the quantification of carotid artery examinations.
Automated carotid segmentation may enhance ultrasound efficiency for atherosclerosis assessment; leaves open prospective validation before clinical adoption.
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 a p -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 at https://github.com/buddykeywordw/Artery-Segments-Recognition .
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Lo et al. (2025) studied Large artery atherosclerosis (n=288). Automated classification model using embedding concatenation of deep image and vision-language models vs. Individual feature sets was evaluated on Classification accuracy of carotid artery segments (p=<0.001). An automated classification model using concatenated embeddings achieved an accuracy of 82.3% for recognizing carotid artery segments, significantly outperforming individual feature sets (p<0.001).
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