Introduction: QCA (Quantitative Coronary Angiography) is an accepted standard for assessing coronary artery stenosis severity. QCA computes the percentage diameter reduction of the lesion, improving upon visual inspection since it provides a numerical quantification of stenosis severity. However, it can be costly, and it requires human input to correct the boundaries of the vessel. AngioNet is a fully-automatic deep learning convolutional neural network trained to identify coronary trees and their vessel diameters in angiograms, and could be a cost-effective alternative to QCA. To explore the accuracy of AngioNet, we compared its ability to identify vessels and their diameter when compared with QCA. Methods: Angiograms with 3-vessel QCA reports from 89 patients acquired at Madras Medical Mission were used. The entire vessel tree was segmented for each angiogram using AngioNet. A fast-marching method was used to calculate the radius along the vessel’s centerline. Vessel diameter was compared in two regions corresponding to the regions marked in the QCA report: the most proximal region, containing the maximum vessel diameter, and the region of stenosis. If no stenosis was present, the distal region containing minimum diameter was selected. The difference in maximum and minimum vessel diameter between QCA and AngioNet was computed, and a Bland-Altman plot (see figure) was used to determine the interchangeability of both methods. Results: The mean absolute difference between both measurements was 0.27mm. The standardized difference was 0.215, corresponding to a 91.5% overlap of their distributions. Lastly, 97% of data points were within the limits of agreement, suggesting minimal clinical differences and that both methods may be interchangeable. Conclusions: Deep learning convolutional networks may be used to assess vessel diameters at a level that agrees well with those of QCA. AngioNet has the potential to automate the detection of stenosis severity.