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Background/Introduction: Cardiovascular diseases (CVDs) are the leading cause of death worldwide, with atherosclerosis being a major contributor. Early detection of carotid artery plaques is crucial for CVD prevention and management. While ultrasound imaging is widely used, its manual analysis is time-consuming and prone to variability. Automating plaque detection with deep learning can improve diagnostic consistency and efficiency. Recent advancements in unsupervised anomaly detection models offer a promising approach to detect plaque-related anomalies in carotid ultrasound images without labeled data. This study is, to the best of our knowledge, the first to apply these models for plaque detection in ultrasound images.

Purpose: This work aims to evaluate the effectiveness of three unsupervised anomaly detection methods—Autoencoder (AE), Variational Autoencoder (VAE), and Anomaly Detection Generative Adversarial Networks (AnoGAN)—for detecting plaques in longitudinal carotid artery ultrasound images. We used carotid ultrasound images from a large Randomized Controlled Trial (RCT) on subclinical atherosclerosis, comprising images from a diverse group of participants collected at both baseline and follow-up periods.

Methods: The generative models were trained to recognize normal anatomical structures using 3,000 plaque-free (normal) images. These models were then tested on 400 images, which included both normal and abnormal images, to distinguish them and detect anomalies in abnormal images. As shown in Figure 1, the anomaly scores were computed by comparing the original images to the generated ones, with a distribution-based cutoff applied to classify the images as normal or abnormal. The threshold for this cutoff was determined based on the distribution of the anomaly scores.

Results: AnoGAN achieved the highest accuracy, as shown in Figure 2, which compares the input image and generated output of AnoGAN, VAE, and AE for an abnormal image. Also, AnoGAN achieved the highest Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC) at 91%, followed by VAE at 79% and AE at 66%. Additionally, AnoGAN achieved the best sensitivity-specificity balance, resulting in the highest AUC-ROC, while VAE demonstrated moderate performance, and AE, despite lower sensitivity, maintained competitive specificity.

Conclusion(s): This study shows the effectiveness of anomaly detection models, especially AnoGAN, in automating the detection of plaques in carotid ultrasound imaging. These models have the potential to reduce diagnostic variability, improve early detection of CVD risk factors, and enhance clinical efficiency. Future research should aim to expand the use of these models to larger datasets and explore their clinical integration to assess real-world applicability. Additionally, optimizing the computational performance and interpretability of these models will be crucial for their widespread adoption in routine clinical settings.