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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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      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                      <jats:italic toggle=\"yes\">n<\/jats:italic>\n                      \u2009=\u2009123) 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).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Bibliometric analysis revealed three publication phases: a formative period (2010\u20132017) with &lt;3\u2009papers\/year; rapid growth (2018\u20132021) culminating in a peak of 28 papers in 2022; and sustained interest into 2025. The United States (\n                      <jats:italic toggle=\"yes\">n<\/jats:italic>\n                      \u2009=\u200939) and China (\n                      <jats:italic toggle=\"yes\">n<\/jats:italic>\n                      \u2009=\u200934) led contributions, and keyword clustering highlighted central themes around \u201cartificial intelligence,\u201d \u201ccoronary artery disease,\u201d and \u201ccomputed tomography angiography.\u201d In disease-specific review, convolutional neural networks (CNNs) and CNN\u2013LSTM hybrids predominated, achieving AUCs from 0.724 to 0.997: for acute myocardial infarction detection, accuracies of 90%\u201395% and AUCs up to 0.99; for ischemic cardiomyopathy differentiation, accuracies of 75%\u201398% and AUCs up to 0.93; and for unstable angina prediction, overall accuracies of 89%\u201395%. Classical machine-learning models (XGBoost and random forest) also showed robust performance (AUC 0.77\u20130.94). Key challenges include dataset heterogeneity, limited multicenter validation, and model interpretability.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/20552076261417142","type":"journal-article","created":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T14:12:58Z","timestamp":1769091178000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Artificial intelligence techniques for cardiovascular disease diagnosis via X-ray sensor-based coronary angiography: A bibliometric and systematic review"],"prefix":"10.1177","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1127-4608","authenticated-orcid":false,"given":"Hao","family":"Ren","sequence":"first","affiliation":[{"name":"City University of Macau"},{"name":"Institute for Healthcare Artificial Intelligence Application, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fengshi","family":"Jing","sequence":"additional","affiliation":[{"name":"City University of Macau"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Fang","sequence":"additional","affiliation":[{"name":"City University of Macau"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9845-6676","authenticated-orcid":false,"given":"Weibin","family":"Cheng","sequence":"additional","affiliation":[{"name":"Institute for Healthcare Artificial Intelligence Application, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,1,22]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"World Health Organization. Cardiovascular diseases (CVDs) [Fact sheet]."},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41572-019-0106-z"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehz425"},{"key":"e_1_3_2_5_2","article-title":"Noninvasive testing for coronary artery disease","volume":"171","author":"Skelly AC","year":"2016","unstructured":"Skelly AC, Hashimoto R, Buckley DI, et al. Noninvasive testing for coronary artery disease. Comp Eff Rev March 2016; 171.","journal-title":"Comp Eff Rev"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11886-022-01655-y"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1161\/01.CIR.53.4.627"},{"key":"e_1_3_2_8_2","unstructured":"Avram R Olgin JE Wan A et al. CathAI: fully automated interpretation of coronary angiograms using neural networks. arXiv preprint arXiv:210607708."},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12410-024-09596-6"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104667"},{"key":"e_1_3_2_11_2","unstructured":"Mirza M Osindero S. 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