ABSTRACT
Coronary Artery Disease (CAD) is commonly diagnosed using X-ray angiography, in which images are taken as radio-opaque dye is flushed through the coronary vessels to visualize the severity of vessel narrowing, or stenosis. Cardiologists typically use visual estimation to approximate the percent diameter reduction of the stenosis, and this directs therapies like stent placement. A fully automatic method to segment the vessels would eliminate potential subjectivity and provide a quantitative and systematic measurement of diameter reduction. Here, we have designed a convolutional neural network, AngioNet, for vessel segmentation in X-ray angiography images. The main innovation in this network is the introduction of an Angiographic Processing Network (APN) which significantly improves segmentation performance on multiple network backbones, with the best performance using Deeplabv3+ (Dice score 0.864, pixel accuracy 0.983, sensitivity 0.918, specificity 0.987). The purpose of the APN is to create an end-to-end pipeline for image pre-processing and segmentation, learning the best possible pre-processing filters to improve segmentation. We have also demonstrated the interchangeability of our network in measuring vessel diameter with Quantitative Coronary Angiography. Our results indicate that AngioNet is a powerful tool for automatic angiographic vessel segmentation that could facilitate systematic anatomical assessment of coronary stenosis in the clinical workflow.
ABSTRACT
Background We systematically reviewed trials comparing different reperfusion strategies for ST-segment-elevation myocardial infarction and used multivariate network meta-analysis to compare outcomes across these strategies. Methods and Results We identified 31 contemporary trials in which patients with ST-segment-elevation myocardial infarction were randomized to ≥2 of the following strategies: fibrinolytic therapy (n=4212), primary percutaneous coronary intervention (PCI) (n=6139), or fibrinolysis followed by routine early PCI (n=5006). We categorized the last approach as "facilitated PCI" when the median time interval between fibrinolysis to PCI was <2 hours (n=2259) and as a "pharmacoinvasive approach" when this interval was ≥2 hours (n=2747). We evaluated outcomes of death, nonfatal reinfarction, stroke, and major bleeding using a multivariate network meta-analysis and a Bayesian analysis. Among the strategies evaluated, primary PCI was associated with the lowest risk of mortality, nonfatal reinfarction, and stroke. For mortality, primary PCI had an odds ratio of 0.73 (95% CI, 0.61-0.89) when compared with fibrinolytic therapy. Of the remaining strategies, the pharmacoinvasive approach was the next most favorable with an odds ratio for death of 0.79 (95% CI, 0.59-1.08) compared with fibrinolytic therapy. The Bayesian model indicated that when the 2 strategies examining routine early invasive therapy following fibrinolysis were directly compared, the probability of adverse outcomes was lower for the pharmacoinvasive approach relative to facilitated PCI. Conclusions A pharmacoinvasive approach is safer and more effective than facilitated PCI and fibrinolytic therapy alone. This has significant implications for ST-segment-elevation myocardial infarction care in settings where timely access to primary PCI, the preferred treatment for ST-segment-elevation myocardial infarction, is not available.
