Convolutional neural networks achieved diagnostic accuracies of 90%-95% and AUCs up to 0.99 for acute myocardial infarction detection using X-ray coronary angiography.
Do artificial intelligence techniques provide accurate diagnosis of cardiovascular diseases via X-ray sensor-based coronary angiography?
Artificial intelligence techniques, particularly deep learning, show high diagnostic accuracy for cardiovascular diseases using coronary angiography, but require robust multicenter validation before clinical integration.
Purpose To systematically evaluate the application of artificial intelligence (AI) techniques in X-ray sensor-based coronary angiography for cardiovascular disease (CVD) diagnosis, mapping publication trends, geographic and topical hotspots via bibliometric analysis, and critically reviewing disease-specific AI methodologies and performance to inform future research and clinical integration. Non-angiographic inputs were considered only when angiography served as the reference standard or when the algorithm was explicitly integrated into an angiography-based workflow. Methods A two-part approach was undertaken. In Part I, we performed a bibliometric analysis of English-language original research and reviews published between 1 June 2010 and 1 June 2025, retrieved from Web of Science, Scopus, and PubMed. Records ( n = 123) were screened using a PRISMA flowchart and analyzed with CiteSpace v6.3.R1 to identify annual publication trends, country contributions, co-authorship networks, and keyword clusters. In Part II, we conducted a structured literature review of the AI methods reported in these studies, organizing findings by three major clinical categories-acute myocardial infarction, ischemic cardiomyopathy, and unstable angina-and extracting model architectures, data sources, and diagnostic performance metrics (accuracy, sensitivity, specificity, and AUC). Results Bibliometric analysis revealed three publication phases: a formative period (2010–2017) with <3 papers/year; rapid growth (2018–2021) culminating in a peak of 28 papers in 2022; and sustained interest into 2025. The United States ( n = 39) and China ( n = 34) led contributions, and keyword clustering highlighted central themes around “artificial intelligence,” “coronary artery disease,” and “computed tomography angiography.” In disease-specific review, convolutional neural networks (CNNs) and CNN–LSTM hybrids predominated, achieving AUCs from 0.724 to 0.997: for acute myocardial infarction detection, accuracies of 90%–95% and AUCs up to 0.99; for ischemic cardiomyopathy differentiation, accuracies of 75%–98% and AUCs up to 0.93; and for unstable angina prediction, overall accuracies of 89%–95%. Classical machine-learning models (XGBoost and random forest) also showed robust performance (AUC 0.77–0.94). Key challenges include dataset heterogeneity, limited multicenter validation, and model interpretability. Conclusion AI, particularly deep-learning frameworks, substantially enhances the accuracy and efficiency of CVD diagnosis via X-ray coronary angiography. However, current evidence is constrained by small single-center datasets, limited external validation, inconsistent leakage safeguards, and scarce calibration/decision-curve reporting. To advance clinical adoption, future efforts should emphasize large-scale, multicenter validation studies, development of explainable AI models, and seamless integration into cardiology workflows.
Ren et al. (2026) studied this question. Convolutional neural networks achieved diagnostic accuracies of 90%-95% and AUCs up to 0.99 for acute myocardial infarction detection using X-ray coronary angiography.