Background: Owing to the differential propensity for bleeding and ischemic events with response to antiplatelet therapy, the safety and effectiveness of potent P2Y12 inhibitor ticagrelor in East Asian populations remain uncertain. Methods: In this multicenter trial, 800 Korean patients hospitalized for acute coronary syndromes with or without ST elevation and intended for invasive management were randomly assigned to receive, in a 1:1 ratio, ticagrelor (180 mg loading dose, 90 mg twice daily thereafter) or clopidogrel (600 mg loading dose, 75 mg daily thereafter). The primary safety outcome was clinically significant bleeding (a composite of major bleeding or minor bleeding according to PLATO (Platelet Inhibition and Patient Outcomes) criteria at 12 months. Results: At 12 months, the incidence of clinically significant bleeding was significantly higher in the ticagrelor group than in the clopidogrel group (11.7% [45/400] vs 5.3% [21/400]; hazard ratio [HR], 2.26; 95% confidence interval [CI], 1.34 to 3.79; P =0.002). The incidences of major bleeding (7.5% [29/400] vs 4.1% [16/400], P =0.04) and fatal bleeding (1% [4/400] vs 0%, P =0.04) were also higher in the ticagrelor group. The incidence of death from cardiovascular causes, myocardial infarction, or stroke was not significantly different between the ticagrelor group and the clopidogrel group (9.2% [36/400] vs 5.8% [23/400]; HR, 1.62; 95% CI, 0.96 to 2.74; P =0.07). Overall safety and effectiveness findings were similar with the use of several different analytic methods and in multiple subgroups. Conclusions: In Korean acute coronary syndrome patients intended to receive early invasive management, standard-dose ticagrelor as compared with clopidogrel was associated with a higher incidence of clinically significant bleeding. The numerically higher incidence of ischemic events should be interpreted with caution, given the present trial was underpowered to draw any conclusion regarding efficacy. Clinical Trial Registration: URL: https://www.clinicaltrials.gov . Unique identifier: NCT02094963.

X-ray coronary angiography is a primary imaging technique for diagnosing coronary diseases. Although quantitative coronary angiography (QCA) provides morphological information of coronary arteries with objective quantitative measures, considerable training is required to identify the target vessels and understand the tree structure of coronary arteries. Despite the use of computer-aided tools, such as the edge-detection method, manual correction is necessary for accurate segmentation of coronary vessels. In the present study, we proposed a robust method for major vessel segmentation using deep learning models with fully convolutional networks. When angiographic images of 3302 diseased major vessels from 2042 patients were tested, deep learning networks accurately identified and segmented the major vessels in X-ray coronary angiography. The average F1 score reached 0.917, and 93.7% of the images exhibited a high F1 score > 0.8. The most narrowed region at the stenosis was distinctly captured with high connectivity. Robust predictability was validated for the external dataset with different image characteristics. For major vessel segmentation, our approach demonstrated that prediction could be completed in real time with minimal image preprocessing. By applying deep learning segmentation, QCA analysis could be further automated, thereby facilitating the use of QCA-based diagnostic methods.