Attention-Based UNet Deep Learning Model for Plaque Segmentation in Carotid Ultrasound for Stroke Risk Stratification: An Artificial Intelligence Paradigm

• Published in: Journal of cardiovascular development and disease Vol. 9; no. 10; p. 326
• Main Authors: Jain, Pankaj K., Dubey, Abhishek, Saba, Luca, Khanna, Narender N., Laird, John R., Nicolaides, Andrew, Fouda, Mostafa M., Suri, Jasjit S., Sharma, Neeraj
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 27.09.2022; MDPI
• Subjects: Artificial intelligence; Atherosclerosis; Automation; Carotid arteries; CCA; CVD; Deep learning; Diabetes; Experiments; ICA; Patients; plaque segmentation; Stroke; Ultrasonic imaging; Veins & arteries; Womens health; United Kingdom > UK; Hong Kong; United States > US; Japan; CCA; CVD; atherosclerosis; deep learning; ICA; Attention-UNet; plaque segmentation; stroke; UNet
• DOI: 10.3390/jcdd9100326
• ISSN: 2308-3425

Stroke and cardiovascular diseases (CVD) significantly affect the world population. The early detection of such events may prevent the burden of death and costly surgery. Conventional methods are neither automated nor clinically accurate. Artificial Intelligence-based methods of automatically detecting and predicting the severity of CVD and stroke in their early stages are of prime importance. This study proposes an attention-channel-based UNet deep learning (DL) model that identifies the carotid plaques in the internal carotid artery (ICA) and common carotid artery (CCA) images. Our experiments consist of 970 ICA images from the UK, 379 CCA images from diabetic Japanese patients, and 300 CCA images from post-menopausal women from Hong Kong. We combined both CCA images to form an integrated database of 679 images. A rotation transformation technique was applied to 679 CCA images, doubling the database for the experiments. The cross-validation K5 (80% training: 20% testing) protocol was applied for accuracy determination. The results of the Attention-UNet model are benchmarked against UNet, UNet++, and UNet3P models. Visual plaque segmentation showed improvement in the Attention-UNet results compared to the other three models. The correlation coefficient (CC) value for Attention-UNet is 0.96, compared to 0.93, 0.96, and 0.92 for UNet, UNet++, and UNet3P models. Similarly, the AUC value for Attention-UNet is 0.97, compared to 0.964, 0.966, and 0.965 for other models. Conclusively, the Attention-UNet model is beneficial in segmenting very bright and fuzzy plaque images that are hard to diagnose using other methods. Further, we present a multi-ethnic, multi-center, racial bias-free study of stroke risk assessment.