Abstract
Background and Objectives: Endothelial dysfunction is an early indicator of cardiovascular disease and is commonly assessed using flow-mediated dilation (FMD). Although handheld ultrasound (HHUS) devices improve measurement accessibility, image analysis for conventional flow-mediated dilation (FMD) assessment remains time-consuming and highly operator-dependent. This study aimed to evaluate the between-day test-retest reliability of an AI-assisted brachial artery image analysis workflow integrating HHUS imaging with a YOLO(v12) deep learning model in postmenopausal women. Materials and Methods: Seventeen postmenopausal women aged 55-70 years completed two flow-mediated dilation assessments conducted seven days apart. Brachial artery images were acquired using a standardized FMD protocol with a handheld ultrasound system. An AI-assisted image analysis workflow based on a YOLO(v12) deep learning model was used to automatically measure baseline diameter (D(base)), peak diameter (D(peak)), absolute FMD (FMD(abs)), and relative FMD (FMD%). Between-day reliability was evaluated using intraclass correlation coefficients (ICCs), coefficients of variation (CVs), and Bland-Altman analysis. Results: Good between-day repeatability was observed for baseline and peak diameters, with ICCs of 0.81 and 0.76 and low CVs (3.26% and 3.22%), respectively. Functional vascular outcomes also demonstrated good reliability, with ICCs of 0.81 for FMD(abs) and 0.87 for FMD%. However, higher CVs were observed for FMD(abs) (17.15%) and FMD% (19.09%), indicating substantial inter-individual variability. Bland-Altman analysis showed a small mean difference for FMD% (0.34%), with no evidence of systematic bias. Conclusions: An AI-assisted HHUS image analysis workflow integrating a YOLO(v12) deep learning model demonstrates acceptable between-day reliability for diameter-based and dilation-based measures of flow-mediated dilation in postmenopausal women. While variability in functional responses exists, the proposed system is feasible for research-oriented vascular assessment, providing a methodological foundation for future validation and clinical translation studies.