An end-to-end framework for intima media measurement and atherosclerotic plaque detection in the carotid artery

Advances in the Application of Artificial Intelligence in the Ultrasound Diagnosis of Vulnerable Carotid Atherosclerotic Plaque

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.

Deep Learning-Based Carotid Plaque Ultrasound Image Detection and Classification Study

Background

This study aimed to develop and evaluate the detection and classification performance of different deep learning models on carotid plaque ultrasound images to achieve efficient and precise ultrasound screening for carotid atherosclerotic plaques.

Methods

This study collected 5611 carotid ultrasound images from 3683 patients from four hospitals between September 17, 2020, and December 17, 2022. By cropping redundant information from the images and annotating them using professional physicians, the dataset was divided into a training set (3927 images) and a test set (1684 images). Four deep learning models, You Only Look Once Version 7 (YOLO V7) and Faster Region-Based Convolutional Neural Network (Faster RCNN) were employed for image detection and classification to distinguish between vulnerable and stable carotid plaques. Model performance was evaluated using accuracy, sensitivity, specificity, F1 score, and area under curve (AUC), with p < 0.05 indicating a statistically significant difference.

Results

We constructed and compared deep learning models based on different network architectures. In the test set, the Faster RCNN (ResNet 50) model exhibited the best classification performance (accuracy (ACC) = 0.88, sensitivity (SEN) = 0.94, specificity (SPE) = 0.71, AUC = 0.91), significantly outperforming the other models. The results suggest that deep learning technology has significant potential for application in detecting and classifying carotid plaque ultrasound images.

Conclusions

The Faster RCNN (ResNet 50) model demonstrated high accuracy and reliability in classifying carotid atherosclerotic plaques, with diagnostic capabilities approaching that of intermediate-level physicians. It has the potential to enhance the diagnostic abilities of primary-level ultrasound physicians and assist in formulating more effective strategies for preventing ischemic stroke.

LSMD: Long-Short Memory-Based Detection Network for Carotid Artery Detection in B-Mode Ultrasound Video Streams

Carotid atherosclerotic plaques are a major complication associated with type II diabetes, and carotid ultrasound is commonly used for diagnosing carotid vascular disease. In primary hospitals, less experienced ultrasound physicians often struggle to consistently capture standard carotid images and identify plaques. To address this issue, we propose a novel approach, the long-short memory-based detection (LSMD) network, for carotid artery detection in ultrasound video streams, facilitating the identification and localization of critical anatomical structures and plaques. This approach models short- and long-distance spatiotemporal features through short-term temporal aggregation (STA) and long-term temporal aggregation (LTA) modules, effectively expanding the temporal receptive field with minimal delay and enhancing the detection efficiency of carotid anatomy and plaques. Specifically, we introduce memory buffers with a dynamic updating strategy to ensure extensive temporal receptive field coverage while minimizing memory and computation costs. The proposed model was trained on 80 carotid ultrasound videos and evaluated on 50, with all videos annotated by physicians for carotid anatomies and plaques. The trained LSMD was evaluated for performance on the validation and test sets using the single-frame image-based single shot multibox detector (SSD) algorithm as a baseline. The results show that the precision, recall, average precision (AP) at ( ), and mean AP (mAP) are 6.83%, 12.29%, 11.23%, and 13.21% higher than the baseline ( ), respectively, while the model's inference latency reaches 6.97 ms on a desktop-level GPU (NVIDIA RTX 3090Ti) and 29.69 ms on an edge computing device (Jetson Orin Nano). These findings demonstrate that LSMD can accurately localize carotid anatomy and plaques with real-time inference, indicating its potential for enhancing diagnostic accuracy in clinical practice.

Fibrinogen/albumin ratio and carotid artery plaques in coronary heart disease patients with different glucose metabolic states: a RCSCD-TCM study

AIM

The relationship between fibrinogen/albumin ratio (FAR) and carotid artery plaques (CAPs) was investigated in patients with coronary heart disease (CHD).

METHODS

A total of 11,624 patients with CHD were enrolled and divided into quartiles based on the FAR (Q1: FAR index ≤ 0.0663; Q2: 0.0664 ≤ FAR index ≤ 0.0790; Q3: 0.0791 ≤ FAR index ≤ 0.0944; Q4: FAR index > 0.0944). Patients were classified into three groups according to their blood glucose levels: normal glucose regulation (NGR), prediabetes mellitus (pre-DM), and diabetes mellitus (DM) groups. Carotid ultrasonography was performed to detect CAPs. The relationship between FAR and CAPs was evaluated using logistic and subgroup analyses.

RESULTS

Among 11,624 participants, 8738 (75.14%) had CAPs. Compared with Q1, the odds ratio (OR) of Q4 in patients with CHD was 2.00 (95% confidence interval [CI]: 1.71-2.34) after multivariate adjustment. Taking Q1 as a reference, a higher OR was observed in Q4 of FAR for CAPs in men [OR: 2.26; 95% CI: 1.73-2.95] in the multi-adjusted models. Moreover, multivariate adjustment indicated that the highest OR was observed in patients with CHD and DM (OR: 2.36; 95% CI: 1.80-3.10).

CONCLUSIONS

A significant association between FAR and CAPs was observed in patients with CHD, regardless of sex or blood glucose levels. Therefore, FAR may be used as an effective indicator to identify patients at a high risk of CAPs among patients with CHD.

Ultrasonic Imaging of Cardiovascular Disease Based on Image Processor Analysis of Hard Plaque Characteristics

Cardiovascular disease detection and analysis using ultrasonic imaging expels errors in manual clinical trials with precise outcomes. It requires a combination of smart computing systems and intelligent image processors. The disease characteristics are analyzed based on the configuration and precise tuning of the processing device. In this article, a characteristic extraction technique (CET) using knowledge learning (KL) is introduced to improve the analysis precision. The proposed method requires optimal selection of disease features and trained similar datasets for improving the characteristic extraction. The disease attributes and accuracy are identified using the standard knowledge update. The image and data features are segmented using the variable processor configuration to prevent false rates. The false rates due to unidentifiable plaque characteristics result in weak knowledge updates. Therefore, the segmentation and data extraction are unanimously performed to prevent feature misleads. The knowledge base is updated using the extracted and identified plaque characteristics for consecutive image analysis. The processor configurations are manageable using the updated knowledge and characteristics to improve precision. The proposed method is verified using precision, characteristic update, training rate, extraction ratio, and time factor.