Screening tools for the detection of coronary artery disease (CAD) are of considerable interest in light of skyrocketing risk factors. Recent work suggests that carotid plaque has a relatively unexplored role in CAD risk prediction but has previously been limited by the difficulty in quantifying its irregular architecture using two-dimensional (2D) ultrasound. The aim of this study was to investigate the utility of a novel automated three-dimensional (3D) ultrasound-based carotid plaque volume quantification technique as a negative predictor of CAD. In this prospective study, 70 consecutive patients referred for coronary angiography underwent same-day 2D and 3D carotid ultrasound scans for the purpose of plaque quantification in the carotid bulbs. Two-dimensional plaque thickness was measured in its maximal value perpendicular to the vessel wall. Total 3D plaque volume was quantified using a stacked-contour method. Luminal narrowing of coronary arteries was analyzed using the established 16-segment model for coronary arteries to produce an overall angiographic score. Receiver operating characteristic curves, negative predictive value, and sensitivity of 2D and 3D plaque quantification relative to coronary angiography were determined. The novel 3D carotid ultrasound method resulted in a higher negative predictive value and sensitivity relative to 2D carotid ultrasound at their optimal thresholds as determined by Youden indices of receiver operating characteristic curves. In particular, total 3D plaque volumes less than the threshold of 0.09 mL accurately predicted the absence of significant CAD in 93.3% of patients (98.0% sensitivity), whereas maximal 2D plaque thickness less than the threshold of 1.35 mm provided significantly lower negative predictability at 75% (93.9% sensitivity). Using the determined threshold of 0.09 mL for plaque volumes, this feasibility study suggests that automated 3D ultrasound-based carotid plaque quantification may serve as an important clinical screening tool to help identify patients who are at low risk for significant CAD.