Abstract WP272: Automatic Detection and Measurement of Atherosclerotic Plaques in Carotid Ultrasound Using Deep Learning

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Purpose: B-Mode ultrasound imaging is commonly used for detection and measurement of atherosclerotic carotid plaques, which are an important cause of ischemic stroke. However, accurate interpretation of ultrasound can be difficult and subjective. Artificial Intelligence (AI) models can assist in image interpretation, reducing subjectivity, and speeding up the process of detection and measurement of carotid plaques. We evaluated the accuracy of a deep learning model for automatic detection of carotid plaques in b-mode ultrasound compared against expert interpretation of the images. Methods: We propose an automated method using convolutional neural networks to detect atherosclerotic plaques and measure intima-media thickness (IMT) in B-Mode carotid images. In contrast to most of the existing methods, our goal was to not only measure IMT in healthy subjects (max IMT below 1.2 mm) but also to provide accurate detection of plaques and other vessel wall pathology. Given the B-mode longitudinal image as the input, the neural network first finds a region of interest (ROI) surrounding the artery and then segments both near wall and far wall of the artery. The network was trained and tested on two separate datasets obtained retrospectively from 3 stroke centers and 4 different ultrasound machine manufacturers. The training dataset was comprised of 1021 images. Results: The performance of the method was assessed on an independent dataset not used for model development to prevent bias, consisting of 205 images, where 54% (111 out of 205) of the images had pathology. The ground truth was determined by an expert reader interpreting images, and Pearson coefficient (IMT correlation) and Bland-Altman analysis were used to assess the performance of the method. The obtained correlation coefficient was 0.93 and r-squared was 0.87, showing a strong correlation. There was no significant over or under estimation of IMT (bias = -0.002 mm, lower limit of agreement (LOA) = -0.246 mm, upper LOA = 0.242 mm). Conclusion: The results show that the proposed deep learning method can be used for accurate analysis and interpretation of carotid ultrasound scans in a clinical setting and potentially reduce the reporting time while increasing objectivity of the reports.