The above automated techniques are based on computer vision and image analysis techniques. \citet{Hirsch2006} applied techniques to segment the vessel from the image in order to determine the vessel orientation. The input images used were duplex color flow images. The color flow information was used to identify the vessel regions. A potential drawback of such an approach is that artifacts in the color flow images can affect subsequent steps in the algorithm. In this paper, we propose a novel automated Doppler angle estimation technique based on a deep-learning framework. The method is designed to operate directly with B-mode (grayscale) images without using any color information. Moreover, the input data to the deep learning network is the raw image without any pre-processing or use of segmentation. Recently, deep learning has been employed in a number of ultrasound applications to improve the clinical workflow : fetal ultrasound to detect the optimal scanning plane \citet{Chen2015}, echocardiography to recognize standard views \citet{Madani_2018}, and reconstructing 3D ultrasound volumes from sequences of freehand images \cite{wein} We hypothesize that the image features necessary for accurate angle estimation are discovered by the deep learning algorithm itself. The longer term motivation is that such a general approach makes the technique more broadly applicable especially when the image quality and appearance is known to vary significantly between ultrasound scanners \citet{Wolstenhulme2015} due to the use of proprietary front-end hardware configurations and custom post-processing software.