PAD is a form of arterial occlusive disease that is challenging to evaluate at the point-of-care. Audible feedback from hand-held dopplers to subjectively assess whether the sound characteristics are consistent with Monophasic, Biphasic, or Triphasic waveforms. This paper presents a Deep Learning system that has the ability to predict waveform phasicity through analysis of doppler sounds. We converted input sound into a spectrogram which visually represents frequency changes over time. A custom trained Convolutional Neural Network (CNN) is used for prediction through learned feature extraction. The system received an F1 score of 90.57% and an accuracy of 96.23%.