Abstract
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.