AI-Enhanced Super-Resolution Handheld Ultrasound for Carotid Plaque Detection in Community Screening

PURPOSE Ischemic stroke is a leading cause of disability and death, and early identification of carotid atherosclerotic plaques is critical for stroke prevention in primary care and community settings. We examined whether artificial intelligence (AI)-enhanced handheld ultrasound (HHUS) could improve carotid plaque detection and stenosis assessment in community-based primary care screening. METHODS We developed a super-resolution reconstruction model (Hyper-CycleGAN) and validated it in a clinical cohort of 127 patients with 198 plaques. The validated model was then applied in a community screening setting with 117 participants and 153 plaques, using portable ultrasound as the reference standard. We evaluated agreement with the reference standard (Bland-Altman, intraclass correlation coefficient) and community diagnostic performance (detection rates, quadratic-weighted Cohen κ, confusion matrices, and standard metrics). RESULTS In community screening, AI-enhanced HHUS identified 94.8% of carotid plaques compared with 87.6% using standard HHUS. Agreement in stenosis grading with the reference standard improved, with weighted κ increasing from 0.492 to 0.836. Sensitivity for identifying ultrasound-defined vulnerable plaques increased from 47.4% to 63.2%, and specificity remained high. CONCLUSIONS AI-enhanced HHUS improved carotid plaque detection and stenosis assessment in community screening, supporting more accurate risk stratification and referral decisions in primary care. This approach could expand access to low-cost stroke prevention strategies in underserved communities.