Far wall plaque segmentation and area measurement in common and internal carotid artery ultrasound using U-series architectures: An unseen Artificial Intelligence paradigm for stroke risk assessment.

Stroke risk assessment using deep learning (DL) requires automated, accurate, and real-time risk assessment while ensuring compact model size. Previous DL paradigms suffered from challenges like memory size, low speed, and complex in nature lacking multi-ethnic, and multi-institution databases. This research segments and measures the area of the plaque far wall of the common carotid (CCA) and internal carotid arteries (ICA) in B-mode ultrasound using four types of solo, namely, UNet, UNet+, UNet++, and UNet+++, and three types of hybrids, namely, Inception-UNet, Fractal-UNet, and Squeeze-UNet, architectures. These seven models are benchmarked against autoencoder-based solution. Three kinds of databases, namely, CCA, ICA, and combined CCA + ICA were implemented using K5 cross-validation protocol. This was validated using unseen Hong Kong data. The CCA database consisted of 379 Japanese images from low-to medium-risk, while the ICA database consisted of 970 Japanese images taken from 97 medium-to high-risk patients. Using the coefficient of correlation (CC) metric between automated measured area and manually delineated area, seven deep learning solo and hybrid models for CCA yielded 0.96, 0.96, 0.98, 0.95, 0.96, and 0.96 respectively, whereas ICA yielded 0.99, 0.99, 0.98, 0.99, 0.98, 0.98, and 0.98 respectively. Area under the receiver operating characteristics curve values for CCA images was 0.97, 0.969, 0.974, 0.969, 0.962, 0.969, and 0.960 respectively, whereas for ICA images were 0.99, 0.989, 0.988, 0.989, 0.986, 0.989, and 0.988, respectively (p < 0.001). The percentage improvement in offline memory size, training time and training parameters for Squeeze-UNet compared to UNet++ were 569%, 122.46%, and 569%, respectively.