Automated analysis of brachial ultrasound image sequences: early detection of cardiovascular disease via surrogates of endothelial function.

Abstract

Early detection of cardiovascular disease would allow timely institution of preventive measures. Arterial endothelium play a primary role in processes leading to the development of atherosclerotic plaque and cardiovascular disease in general. Determination of flow-mediated dilatation (FMD) of brachial arteries from B-mode ultrasound image sequences offers a noninvasive surrogate index of endothelial function. A highly automated method for analysis of brachial ultrasound image sequences is reported and its performance assessed. The method overcomes the variability of brachial ultrasound images across subjects by incorporating machine learning and quality control steps. The automated method outperformed conventional manual analysis by providing a decreased analysis bias, increased reproducibility, and improved measurement accuracy. Consequently, it decreases inter- and intraobserver as well interinstitution variability. The method has been employed in a number of population studies with thousands of subjects analyzed.