{ "abstract": "The 12-lead ECG is central to the evaluation of patients with suspected coronary artery disease (CAD) but has limited diagnostic accuracy for identifying significant obstructive disease prior to elective coronary angiography. We previously developed an artificial intelligence–enhanced ECG (AI-ECG) model for detecting acute coronary occlusion. Whether this model generalizes to identifying angiographically significant CAD in symptomatic patients undergoing elective evaluation remains unknown. In this prospective cohort study, symptomatic outpatients undergoing elective coronary angiography at a tertiary center received standard 12-lead ECGs before the procedure. ECGs were analyzed offline using a previously validated AI-ECG model originally developed for acute coronary occlusion. Patients were categorized as low or intermediate-to-high risk using predefined thresholds. The primary outcome was angiographically significant CAD (≥70% stenosis in a major epicardial vessel or ≥50% stenosis of the left main coronary artery). Among 363 patients (age 58.9 ± 11 years; 55.1% male), 36.1% had angiographically significant CAD. The AI-ECG classified 56% as low risk and 44% as intermediate-to-high risk. After adjustment for conventional clinical characteristics, intermediate-to-high risk remained independently associated with significant CAD (OR 3.12, 95% CI 1.88–5.20; p < 0.001). Model discrimination was good (AUROC 0.79, 95% CI 0.74–0.84), with 60% precision at 80% recall. In summary, the ECG-SMART AI score demonstrated good diagnostic performance for identifying angiographically significant CAD in symptomatic patients undergoing elective coronary angiography. These findings support the generalizability of a previously developed AI-ECG model beyond acute coronary occlusion and suggest that AI-enhanced ECG analysis may complement existing clinical assessment by providing additional noninvasive information to refine pretest probability during elective evaluation. Author summary: Coronary artery disease is one of the leading causes of illness and death worldwide. Identifying patients with important narrowing of the heart arteries can be challenging, particularly when symptoms are not acute. The electrocardiogram is inexpensive, widely available, and routinely used in cardiovascular care, but conventional interpretation has limited ability to detect underlying coronary artery narrowing in stable patients. We previously developed an artificial intelligence approach that analyzes information within a standard 12-lead electrocardiogram to identify patients at risk for an acutely blocked coronary artery. In this study, we asked whether the same approach could provide useful information in a very different setting: symptomatic patients undergoing planned coronary angiography rather than emergency evaluation for a heart attack. Among 363 patients, the artificial intelligence score successfully identified patients more likely to have significant coronary artery narrowing and remained informative after accounting for conventional cardiovascular risk factors. These findings suggest that artificial intelligence may uncover subtle electrical patterns associated with coronary disease that extend beyond acute heart attacks. Further studies are needed to determine whether this approach can help clinicians improve noninvasive evaluation and guide decisions about additional testing in broader patient populations.", "abstract_shown": true, "affiliations": [ "School of Nursing, Middle East University, Amman, Jordan", "School of Nursing, University of Jordan, Amman, Jordan", "Department of Cardiology, Jordan University Hospital, Amman, Jordan", "School of Nursing, University of Rochester, Rochester, New York, United States of America", "School of Engineering, Texas Tech University, Lubbock, Texas, United States of America", "Department of Electrical & Computer Engineering, University of Toronto, Ontario, Canada", "Division of Cardiology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America", "INTELLICARDIO INC., Pittsburgh, Pennsylvania, United States of America", "Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America" ], "article_type": "Research Article", "arxiv": null, "author_total": 10, "authors": [ { "affs": [ 0 ], "name": "Dania A. Bani Hani" }, { "affs": [ 1 ], "name": "Jafar A. Alshraideh" }, { "affs": [ 2 ], "name": "Hamza Alduraidi" }, { "affs": [ 3 ], "name": "Akram Saleh" }, { "affs": [ 4 ], "name": "Nathan T. Riek" }, { "affs": [ 5 ], "name": "Karam Daoud" }, { "affs": [ 6 ], "name": "Rui Qi Ji" }, { "affs": [ 7 ], "name": "Samir Saba" }, { "affs": [ 8 ], "name": "Clifton W. Callaway" }, { "name": "Salah Al-Zaiti" } ], "bibtex": "https://research.draconitic.com/paper/8njg31740hgmz.bib", "cited_by": 0, "doi": "10.1371/journal.pdig.0001701", "fulltext_url": "https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001701", "id": "8njg31740hgmz", "issns": [ "2767-3170" ], "landing_url": "https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001701", "language": null, "licence": "CC-BY-4.0", "licence_label": "CC BY 4.0", "licence_source": "journal", "licence_url": "https://creativecommons.org/licenses/by/4.0/", "metadata_licence": null, "native_subjects": [ [ "openalex", "2705|ECG Monitoring and Analysis" ], [ "plos", "/Computer and information sciences/Artificial intelligence" ], [ "plos", "/Medicine and health sciences/Cardiology/Cardiovascular medicine/Cardiovascular diseases/Cardiovascular disease risk" ], [ "plos", "/Medicine and health sciences/Cardiology/Cardiovascular medicine/Cardiovascular diseases/Coronary heart disease" ], [ "plos", "/Medicine and health sciences/Cardiology/Cardiovascular medicine/Cardiovascular imaging/Angiography" ], [ "plos", "/Medicine and health sciences/Cardiology/Myocardial infarction" ], [ "plos", "/Medicine and health sciences/Diagnostic medicine/Diagnostic radiology/Cardiovascular imaging/Angiography" ], [ "plos", "/Medicine and health sciences/Endocrinology/Endocrine disorders/Diabetes mellitus" ], [ "plos", "/Medicine and health sciences/Epidemiology/Medical risk factors" ], [ "plos", "/Medicine and health sciences/Medical conditions/Cardiovascular diseases/Cardiovascular disease risk" ], [ "plos", "/Medicine and health sciences/Medical conditions/Cardiovascular diseases/Coronary heart disease" ], [ "plos", "/Medicine and health sciences/Medical conditions/Metabolic disorders/Diabetes mellitus" ], [ "plos", "/Medicine and health sciences/Radiology and imaging/Diagnostic radiology/Cardiovascular imaging/Angiography" ], [ "plos", "/Medicine and health sciences/Vascular medicine/Coronary heart disease" ], [ "plos", "/Research and analysis methods/Bioassays and physiological analysis/Electrophysiological techniques/Cardiac electrophysiology/Electrocardiography" ], [ "plos", "/Research and analysis methods/Imaging techniques/Diagnostic radiology/Cardiovascular imaging/Angiography" ] ], "openalex": "W7220960268", "pdf_url": "https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0001701&type=printable", "pmcid": null, "pmid": null, "published": "2026-10-08", "publisher": "Public Library of Science", "ris": "https://research.draconitic.com/paper/8njg31740hgmz.ris", "sources": [ { "first_seen": "2026-10-30T12:17:30Z", "native_id": "10.1371/journal.pdig.0001701", "source": "plos", "url": "https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001701" } ], "subjects": [ { "field": "medicine", "origin": "plos:cardiovascular diseases", "subfield": "cardiovascular" }, { "field": "medicine", "origin": "plos:diagnostic radiology", "subfield": "imaging" } ], "title": "Diagnostic performance of the ECG-SMART AI score for detecting angiographically significant coronary artery disease in patients undergoing elective coronary angiography", "title_text": "Diagnostic performance of the ECG-SMART AI score for detecting angiographically significant coronary artery disease in patients undergoing elective coronary angiography", "type": "peer-reviewed", "updated_at": "2026-10-30T12:17:30Z", "url": "https://research.draconitic.com/paper/8njg31740hgmz", "venue": "PLOS Digital Health", "versions": [] }