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Herman R, Meyers HP, Smith SW, Bertolone DT, Leone A, Bermpeis K, Viscusi MM, Belmonte M, Demolder A, Boza V, Vavrik B, Kresnakova V, Iring A, Martonak M, Bahyl J, Kisova T, Schelfaut D, Vanderheyden M, Perl L, Aslanger EK, Hatala R, Wojakowski W, Bartunek J, Barbato E. International evaluation of an artificial intelligence-powered electrocardiogram model detecting acute coronary occlusion myocardial infarction. Eur Heart J Digit Health 2024; 5:123-133. [PMID: 38505483 PMCID: PMC10944682 DOI: 10.1093/ehjdh/ztad074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/13/2023] [Accepted: 11/02/2023] [Indexed: 03/21/2024]
Abstract
Aims A majority of acute coronary syndromes (ACS) present without typical ST elevation. One-third of non-ST-elevation myocardial infarction (NSTEMI) patients have an acutely occluded culprit coronary artery [occlusion myocardial infarction (OMI)], leading to poor outcomes due to delayed identification and invasive management. In this study, we sought to develop a versatile artificial intelligence (AI) model detecting acute OMI on single-standard 12-lead electrocardiograms (ECGs) and compare its performance with existing state-of-the-art diagnostic criteria. Methods and results An AI model was developed using 18 616 ECGs from 10 543 patients with suspected ACS from an international database with clinically validated outcomes. The model was evaluated in an international cohort and compared with STEMI criteria and ECG experts in detecting OMI. The primary outcome of OMI was an acutely occluded or flow-limiting culprit artery requiring emergent revascularization. In the overall test set of 3254 ECGs from 2222 patients (age 62 ± 14 years, 67% males, 21.6% OMI), the AI model achieved an area under the curve of 0.938 [95% confidence interval (CI): 0.924-0.951] in identifying the primary OMI outcome, with superior performance [accuracy 90.9% (95% CI: 89.7-92.0), sensitivity 80.6% (95% CI: 76.8-84.0), and specificity 93.7 (95% CI: 92.6-94.8)] compared with STEMI criteria [accuracy 83.6% (95% CI: 82.1-85.1), sensitivity 32.5% (95% CI: 28.4-36.6), and specificity 97.7% (95% CI: 97.0-98.3)] and with similar performance compared with ECG experts [accuracy 90.8% (95% CI: 89.5-91.9), sensitivity 73.0% (95% CI: 68.7-77.0), and specificity 95.7% (95% CI: 94.7-96.6)]. Conclusion The present novel ECG AI model demonstrates superior accuracy to detect acute OMI when compared with STEMI criteria. This suggests its potential to improve ACS triage, ensuring appropriate and timely referral for immediate revascularization.
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Affiliation(s)
- Robert Herman
- Department of Advanced Biomedical Sciences, University of Naples Federico II, C.so Umberto I, 40, 80138 Naples, Italy
- Cardiovascular Centre Aalst, OLV Hospital, Moorselbaan 164, Aalst 9300, Belgium
- Powerful Medical, Bratislavska 81/37, 931 01 Samorin, Slovakia
| | | | - Stephen W Smith
- Department of Emergency Medicine, University of Minnesota, Minneapolis, MN, USA
- Department of Emergency Medicine, Hennepin Healthcare, Minneapolis, MN, USA
| | - Dario T Bertolone
- Department of Advanced Biomedical Sciences, University of Naples Federico II, C.so Umberto I, 40, 80138 Naples, Italy
- Cardiovascular Centre Aalst, OLV Hospital, Moorselbaan 164, Aalst 9300, Belgium
| | - Attilio Leone
- Department of Advanced Biomedical Sciences, University of Naples Federico II, C.so Umberto I, 40, 80138 Naples, Italy
- Cardiovascular Centre Aalst, OLV Hospital, Moorselbaan 164, Aalst 9300, Belgium
| | - Konstantinos Bermpeis
- Department of Advanced Biomedical Sciences, University of Naples Federico II, C.so Umberto I, 40, 80138 Naples, Italy
- Cardiovascular Centre Aalst, OLV Hospital, Moorselbaan 164, Aalst 9300, Belgium
| | - Michele M Viscusi
- Department of Advanced Biomedical Sciences, University of Naples Federico II, C.so Umberto I, 40, 80138 Naples, Italy
- Cardiovascular Centre Aalst, OLV Hospital, Moorselbaan 164, Aalst 9300, Belgium
| | - Marta Belmonte
- Department of Advanced Biomedical Sciences, University of Naples Federico II, C.so Umberto I, 40, 80138 Naples, Italy
- Cardiovascular Centre Aalst, OLV Hospital, Moorselbaan 164, Aalst 9300, Belgium
| | | | - Vladimir Boza
- Powerful Medical, Bratislavska 81/37, 931 01 Samorin, Slovakia
- Faculty of Mathematics, Physics and Informatics, Comenius University in Bratislava, Bratislava, Slovakia
| | - Boris Vavrik
- Powerful Medical, Bratislavska 81/37, 931 01 Samorin, Slovakia
| | - Viera Kresnakova
- Powerful Medical, Bratislavska 81/37, 931 01 Samorin, Slovakia
- Department of Cybernetics and Artificial Intelligence, Technical University of Kosice, Kosice, Slovakia
| | - Andrej Iring
- Powerful Medical, Bratislavska 81/37, 931 01 Samorin, Slovakia
| | - Michal Martonak
- Powerful Medical, Bratislavska 81/37, 931 01 Samorin, Slovakia
| | - Jakub Bahyl
- Powerful Medical, Bratislavska 81/37, 931 01 Samorin, Slovakia
| | - Timea Kisova
- Powerful Medical, Bratislavska 81/37, 931 01 Samorin, Slovakia
- Faculty of Medicine and Dentistry, Barts and The London School of Medicine and Dentistry, London, UK
| | - Dan Schelfaut
- Cardiovascular Centre Aalst, OLV Hospital, Moorselbaan 164, Aalst 9300, Belgium
| | - Marc Vanderheyden
- Cardiovascular Centre Aalst, OLV Hospital, Moorselbaan 164, Aalst 9300, Belgium
| | - Leor Perl
- Department of Cardiology, Rabin Medical Center, Petah Tikvah, Israel
| | - Emre K Aslanger
- Department of Cardiology, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Robert Hatala
- Department of Arrhythmia and Pacing, National Institute of Cardiovascular Diseases, Bratislava, Slovakia
| | - Wojtek Wojakowski
- Department of Cardiology and Structural Heart Diseases, Medical University of Silesia, Katowice, Poland
| | - Jozef Bartunek
- Cardiovascular Centre Aalst, OLV Hospital, Moorselbaan 164, Aalst 9300, Belgium
| | - Emanuele Barbato
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Psychology, Sapienza University of Rome, Rome, Italy
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Herman R, Demolder A, Vavrik B, Martonak M, Boza V, Kresnakova V, Iring A, Palus T, Bahyl J, Nelis O, Beles M, Fabbricatore D, Perl L, Bartunek J, Hatala R. Validation of an automated artificial intelligence system for 12‑lead ECG interpretation. J Electrocardiol 2024; 82:147-154. [PMID: 38154405 DOI: 10.1016/j.jelectrocard.2023.12.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 12/14/2023] [Accepted: 12/18/2023] [Indexed: 12/30/2023]
Abstract
BACKGROUND The electrocardiogram (ECG) is one of the most accessible and comprehensive diagnostic tools used to assess cardiac patients at the first point of contact. Despite advances in computerized interpretation of the electrocardiogram (CIE), its accuracy remains inferior to physicians. This study evaluated the diagnostic performance of an artificial intelligence (AI)-powered ECG system and compared its performance to current state-of-the-art CIE. METHODS An AI-powered system consisting of 6 deep neural networks (DNN) was trained on standard 12‑lead ECGs to detect 20 essential diagnostic patterns (grouped into 6 categories: rhythm, acute coronary syndrome (ACS), conduction abnormalities, ectopy, chamber enlargement and axis). An independent test set of ECGs with diagnostic consensus of two expert cardiologists was used as a reference standard. AI system performance was compared to current state-of-the-art CIE. The key metrics used to compare performances were sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score. RESULTS A total of 932,711 standard 12‑lead ECGs from 173,949 patients were used for AI system development. The independent test set pooled 11,932 annotated ECG labels. In all 6 diagnostic categories, the DNNs achieved high F1 scores: Rhythm 0.957, ACS 0.925, Conduction abnormalities 0.893, Ectopy 0.966, Chamber enlargement 0.972, and Axis 0.897. The diagnostic performance of DNNs surpassed state-of-the-art CIE for the 13 out of 20 essential diagnostic patterns and was non-inferior for the remaining individual diagnoses. CONCLUSIONS Our results demonstrate the AI-powered ECG model's ability to accurately identify electrocardiographic abnormalities from the 12‑lead ECG, highlighting its potential as a clinical tool for healthcare professionals.
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Affiliation(s)
- Robert Herman
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy; Cardiovascular Centre Aalst, Aalst, Belgium; Powerful Medical, Bratislava, Slovakia.
| | | | | | | | - Vladimir Boza
- Powerful Medical, Bratislava, Slovakia; Faculty of Mathematics, Physics and Informatics, Comenius University in Bratislava, Bratislava, Slovakia
| | - Viera Kresnakova
- Powerful Medical, Bratislava, Slovakia; Department of Cybernetics and Artificial Intelligence, Technical University of Kosice, Kosice, Slovakia
| | | | | | | | | | | | | | - Leor Perl
- Department of Cardiology, Rabin Medical Center, Petah Tikvah, Israel
| | | | - Robert Hatala
- Department of Arrhythmia and Pacing, National Institute of Cardiovascular Diseases, Bratislava, Slovakia.
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