{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T17:10:22Z","timestamp":1781370622191,"version":"3.54.1"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"DOI":"10.7759\/cureus.95059","type":"journal-article","created":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T14:40:17Z","timestamp":1761057617000},"source":"Crossref","is-referenced-by-count":1,"title":["The Digital Revolution in Cardiac Ischemia: Artificial Intelligence (AI)-Enhanced Detection, Diagnosis, and Risk Stratification"],"prefix":"10.7759","author":[{"given":"Ahmed","family":"Khalifa","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mostafa","family":"Abdulaziz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Syed Shahzil","family":"Parvez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed","family":"Salem","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hafsa S","family":"Panhwer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Usman","family":"Saleem","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahmoud","family":"Eldeeb","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tariq","family":"Weld-Ali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maryam","family":"Mosaad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01410-3","article-title":"A multitask deep learning model utilizing electrocardiograms for major cardiovascular adverse events prediction","volume":"8","author":"Lin CH","year":"2025","unstructured":"Lin CH, Liu ZY, Chu PH, et al.. A multitask deep learning model utilizing electrocardiograms for major cardiovascular adverse events prediction. NPJ Digit Med. 2025, 8:1. 10.1038\/s41746-024-01410-3","journal-title":"NPJ Digit Med"},{"key":"ref2","unstructured":"Global health estimates. life expectancy and leading causes of death and disability. (2025). Accessed: September 18, 2025: https:\/\/www.who.int\/data\/gho\/data\/themes\/theme-details\/GHO\/mortality-and-global-health-estimates."},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcmg.2020.05.039","article-title":"Impact of early revascularization on major adverse cardiovascular events in relation to automatically quantified ischemia","volume":"14","author":"Azadani PN","year":"2021","unstructured":"Azadani PN, Miller RJ, Sharir T, et al.. Impact of early revascularization on major adverse cardiovascular events in relation to automatically quantified ischemia. JACC Cardiovasc Imaging. 2021, 14:644-53. 10.1016\/j.jcmg.2020.05.039","journal-title":"JACC Cardiovasc Imaging"},{"key":"ref4","doi-asserted-by":"crossref","DOI":"10.15420\/icr.2018.16.2","article-title":"Risk stratification in patients with coronary artery disease: a practical walkthrough in the landscape of prognostic risk models","volume":"13","author":"Buccheri S","year":"2018","unstructured":"Buccheri S, D'Arrigo P, Franchina G, Capodanno D. Risk stratification in patients with coronary artery disease: a practical walkthrough in the landscape of prognostic risk models. Interv Cardiol. 2018, 13:112-20.","journal-title":"Interv Cardiol"},{"key":"ref5","unstructured":"Acute myocardial infarction (MI). (2025). Accessed. September 18, 2025: https:\/\/www.msdmanuals.com\/professional\/cardiovascular-disorders\/coronary-artery-disease\/acute-myocardial-infarction-mi."},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.annemergmed.2024.11.019","article-title":"ECG patterns of occlusion myocardial infarction: a narrative review","volume":"85","author":"Ricci F","year":"2025","unstructured":"Ricci F, Martini C, Scordo DM, et al.. ECG patterns of occlusion myocardial infarction: a narrative review. Ann Emerg Med. 2025, 85:330-40. 10.1016\/j.annemergmed.2024.11.019","journal-title":"Ann Emerg Med"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijcha.2020.100603","article-title":"DIagnostic accuracy oF electrocardiogram for acute coronary OCClUsion resuLTing in myocardial infarction (DIFOCCULT Study)","volume":"30","author":"Aslanger EK","year":"2020","unstructured":"Aslanger EK, Y\u0131ld\u0131r\u0131mt\u00fcrk \u00d6, \u015eim\u015fek B, et al.. DIagnostic accuracy oF electrocardiogram for acute coronary OCClUsion resuLTing in myocardial infarction (DIFOCCULT Study). Int J Cardiol Heart Vasc. 2020, 30:100603. 10.1016\/j.ijcha.2020.100603","journal-title":"Int J Cardiol Heart Vasc"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijcha.2021.100767","article-title":"Accuracy of OMI ECG findings versus STEMI criteria for diagnosis of acute coronary occlusion myocardial infarction","volume":"33","author":"Pendell Meyers H","year":"2021","unstructured":"Pendell Meyers H, Bracey A, Lee D, et al.. Accuracy of OMI ECG findings versus STEMI criteria for diagnosis of acute coronary occlusion myocardial infarction. Int J Cardiol Heart Vasc. 2021, 33:100767. 10.1016\/j.ijcha.2021.100767","journal-title":"Int J Cardiol Heart Vasc"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacadv.2024.101314","article-title":"From ST-segment elevation MI to occlusion MI: the new paradigm shift in acute myocardial infarction","volume":"3","author":"McLaren J","year":"2024","unstructured":"McLaren J, de Alencar JN, Aslanger EK, Meyers HP, Smith SW. From ST-segment elevation MI to occlusion MI: the new paradigm shift in acute myocardial infarction. JACC Adv. 2024, 3:101314. 10.1016\/j.jacadv.2024.101314","journal-title":"JACC Adv"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/s12350-018-1407-4","article-title":"Inter-reader variability of SPECT MPI readings in low- and middle-income countries: results from the IAEA-MPI Audit Project (I-MAP)","volume":"27","author":"Dondi M","year":"2020","unstructured":"Dondi M, Rodella C, Giubbini R, et al.. Inter-reader variability of SPECT MPI readings in low- and middle-income countries: results from the IAEA-MPI Audit Project (I-MAP). J Nucl Cardiol. 2020, 27:465-78. 10.1007\/s12350-018-1407-4","journal-title":"J Nucl Cardiol"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/S0025-6196(11)64659-4","article-title":"Stress echocardiography. Part I. Exercise echocardiography: techniques, implementation, clinical applications, and correlations","volume":"70","author":"Roger VL","year":"1995","unstructured":"Roger VL, Pellikka PA, Oh JK, et al.. Stress echocardiography. Part I. Exercise echocardiography: techniques, implementation, clinical applications, and correlations. Mayo Clin Proc. 1995, 70:5-15. 10.1016\/S0025-6196(11)64659-4","journal-title":"Mayo Clin Proc"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.gheart.2013.01.001","article-title":"Cardiovascular disease risk assessment: insights from Framingham","volume":"8","author":"D'Agostino RB Sr","year":"2013","unstructured":"D'Agostino RB Sr, Pencina MJ, Massaro JM, Coady S. Cardiovascular disease risk assessment: insights from Framingham. Glob Heart. 2013, 8:11-23. 10.1016\/j.gheart.2013.01.001","journal-title":"Glob Heart"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.i2416","article-title":"Prediction models for cardiovascular disease risk in the general population: systematic review","volume":"353","author":"Damen JA","year":"2016","unstructured":"Damen JA, Hooft L, Schuit E, et al.. Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ. 2016, 353:i2416. 10.1136\/bmj.i2416","journal-title":"BMJ"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1161\/01.cir.97.18.1837","article-title":"Prediction of coronary heart disease using risk factor categories","volume":"97","author":"Wilson PW","year":"1998","unstructured":"Wilson PW, D'Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation. 1998, 97:1837-47. 10.1161\/01.cir.97.18.1837","journal-title":"Circulation"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1038\/s41569-020-00503-2","article-title":"Artificial intelligence-enhanced electrocardiography in cardiovascular disease management","volume":"18","author":"Siontis KC","year":"2021","unstructured":"Siontis KC, Noseworthy PA, Attia ZI, Friedman PA. Artificial intelligence-enhanced electrocardiography in cardiovascular disease management. Nat Rev Cardiol. 2021, 18:465-78. 10.1038\/s41569-020-00503-2","journal-title":"Nat Rev Cardiol"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0268-3","article-title":"Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network","volume":"25","author":"Hannun AY","year":"2019","unstructured":"Hannun AY, Rajpurkar P, Haghpanahi M, Tison GH, Bourn C, Turakhia MP, Ng AY. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med. 2019, 25:65-9. 10.1038\/s41591-018-0268-3","journal-title":"Nat Med"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1038\/s41569-025-01191-6","article-title":"Coronary CT angiography evaluation with artificial intelligence for individualized medical treatment of atherosclerosis: a Consensus Statement from the QCI Study Group","author":"Schulze K","year":"2025","unstructured":"Schulze K, Stantien AM, Williams MC, et al.. Coronary CT angiography evaluation with artificial intelligence for individualized medical treatment of atherosclerosis: a Consensus Statement from the QCI Study Group. Nat Rev Cardiol. 2025, 10.1038\/s41569-025-01191-6","journal-title":"Nat Rev Cardiol"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1038\/s44325-024-00031-9","article-title":"Artificial intelligence bias in the prediction and detection of cardiovascular disease","volume":"1","author":"Mihan A","year":"2024","unstructured":"Mihan A, Pandey A, Van Spall HGC. Artificial intelligence bias in the prediction and detection of cardiovascular disease. npj Cardiovasc Health. 2024, 1:1-8. 10.1038\/s44325-024-00031-9","journal-title":"npj Cardiovasc Health"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.3390\/jpm13091421","article-title":"AI-driven decision support for early detection of cardiac events: unveiling patterns and predicting myocardial ischemia","volume":"13","author":"Elvas LB","year":"2023","unstructured":"Elvas LB, Nunes M, Ferreira JC, Dias MS, Ros\u00e1rio LB. AI-driven decision support for early detection of cardiac events: unveiling patterns and predicting myocardial ischemia. J Pers Med. 2023, 13:1421. 10.3390\/jpm13091421","journal-title":"J Pers Med"},{"key":"ref20","unstructured":"Artificial intelligence in ECG diagnostics\u2014where are we now?. (2025). Accessed. September 18, 2025: https:\/\/www.escardio.org\/Councils\/Council-for-Cardiology-Practice-(CCP)\/Cardiopractice\/artificial-intelligence-in-ecg...."},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2022.860032","article-title":"Deep learning for detecting and locating myocardial infarction by electrocardiogram: a literature review","volume":"9","author":"Xiong P","year":"2022","unstructured":"Xiong P, Lee SM, Chan G. Deep learning for detecting and locating myocardial infarction by electrocardiogram: a literature review. Front Cardiovasc Med. 2022, 9:860032. 10.3389\/fcvm.2022.860032","journal-title":"Front Cardiovasc Med"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-023-02396-3","article-title":"Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction","volume":"29","author":"Al-Zaiti SS","year":"2023","unstructured":"Al-Zaiti SS, Martin-Gill C, Z\u00e8gre-Hemsey JK, et al.. Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction. Nat Med. 2023, 29:1804-13. 10.1038\/s41591-023-02396-3","journal-title":"Nat Med"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1093\/ehjdh\/ztad074","article-title":"International evaluation of an artificial intelligence-powered electrocardiogram model detecting acute coronary occlusion myocardial infarction","volume":"5","author":"Herman R","year":"2024","unstructured":"Herman R, Meyers HP, Smith SW, et al.. International evaluation of an artificial intelligence-powered electrocardiogram model detecting acute coronary occlusion myocardial infarction. Eur Heart J Digit Health. 2024, 5:123-33. 10.1093\/ehjdh\/ztad074","journal-title":"Eur Heart J Digit Health"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-17804-2","article-title":"Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram","volume":"11","author":"Al-Zaiti S","year":"2020","unstructured":"Al-Zaiti S, Besomi L, Bouzid Z, et al.. Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram. Nat Commun. 2020, 11:3966. 10.1038\/s41467-020-17804-2","journal-title":"Nat Commun"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.4244\/EIJ-D-20-01155","article-title":"A deep learning algorithm for detecting acute myocardial infarction","volume":"17","author":"Liu WC","year":"2021","unstructured":"Liu WC, Lin CS, Tsai CS, et al.. A deep learning algorithm for detecting acute myocardial infarction. EuroIntervention. 2021, 17:765-73. 10.4244\/EIJ-D-20-01155","journal-title":"EuroIntervention"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.3389\/fphys.2022.783184","article-title":"Detection and localization of myocardial infarction based on multi-scale ResNet and attention mechanism","volume":"13","author":"Cao Y","year":"2022","unstructured":"Cao Y, Liu W, Zhang S, et al.. Detection and localization of myocardial infarction based on multi-scale ResNet and attention mechanism. Front Physiol. 2022, 13:783184. 10.3389\/fphys.2022.783184","journal-title":"Front Physiol"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering9090430","article-title":"End-to-end convolutional neural network model to detect and localize myocardial infarction using 12-lead ECG images without preprocessing","volume":"9","author":"Uchiyama R","year":"2022","unstructured":"Uchiyama R, Okada Y, Kakizaki R, Tomioka S. End-to-end convolutional neural network model to detect and localize myocardial infarction using 12-lead ECG images without preprocessing. Bioengineering (Basel). 2022, 9:430. 10.3390\/bioengineering9090430","journal-title":"Bioengineering (Basel)"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2023.102259","article-title":"Identification and risk stratification of coronary disease by artificial intelligence-enabled ECG","volume":"65","author":"Awasthi S","year":"2023","unstructured":"Awasthi S, Sachdeva N, Gupta Y, et al.. Identification and risk stratification of coronary disease by artificial intelligence-enabled ECG. EClinicalMedicine. 2023, 65:102259. 10.1016\/j.eclinm.2023.102259","journal-title":"EClinicalMedicine"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0240-2","article-title":"Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram","volume":"25","author":"Attia ZI","year":"2019","unstructured":"Attia ZI, Kapa S, Lopez-Jimenez F, et al.. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med. 2019, 25:70-4. 10.1038\/s41591-018-0240-2","journal-title":"Nat Med"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijcha.2024.101573","article-title":"Smartwatch ECG and artificial intelligence in detecting acute coronary syndrome compared to traditional 12-lead ECG","volume":"56","author":"Choi J","year":"2025","unstructured":"Choi J, Kim J, Spaccarotella C, Esposito G, Oh IY, Cho Y, Indolfi C. Smartwatch ECG and artificial intelligence in detecting acute coronary syndrome compared to traditional 12-lead ECG. Int J Cardiol Heart Vasc. 2025, 56:101573. 10.1016\/j.ijcha.2024.101573","journal-title":"Int J Cardiol Heart Vasc"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.jelectrocard.2024.02.011","article-title":"Spotting myocardial ischemia on a smartwatch","volume":"84","author":"Tyler KR","year":"2024","unstructured":"Tyler KR, Mumma BE, Anderson DR, Goldschlager N. Spotting myocardial ischemia on a smartwatch. J Electrocardiol. 2024, 84:70-4. 10.1016\/j.jelectrocard.2024.02.011","journal-title":"J Electrocardiol"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-15432-4","article-title":"Automatic diagnosis of the 12-lead ECG using a deep neural network","volume":"11","author":"Ribeiro AH","year":"2020","unstructured":"Ribeiro AH, Ribeiro MH, Paix\u00e3o GM, et al.. Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat Commun. 2020, 11:1760. 10.1038\/s41467-020-15432-4","journal-title":"Nat Commun"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehaf004","article-title":"Artificial intelligence applied to electrocardiogram to rule out acute myocardial infarction: the ROMIAE multicentre study","volume":"46","author":"Lee MS","year":"2025","unstructured":"Lee MS, Shin TG, Lee Y, et al.. Artificial intelligence applied to electrocardiogram to rule out acute myocardial infarction: the ROMIAE multicentre study. Eur Heart J. 2025, 46:1917-29. 10.1093\/eurheartj\/ehaf004","journal-title":"Eur Heart J"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacadv.2025.102011","article-title":"Fully automated diagnosis of acute myocardial infarction using electrocardiograms and multimodal deep learning","volume":"4","author":"Hilgendorf L","year":"2025","unstructured":"Hilgendorf L, Petursson P, Andersson E, et al.. Fully automated diagnosis of acute myocardial infarction using electrocardiograms and multimodal deep learning. JACC Adv. 2025, 4:102011. 10.1016\/j.jacadv.2025.102011","journal-title":"JACC Adv"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.121.024198","article-title":"Information extraction from electronic health records to predict readmission following acute myocardial infarction: does natural language processing using clinical notes improve prediction of readmission?","volume":"11","author":"Brown JR","year":"2022","unstructured":"Brown JR, Ricket IM, Reeves RM, et al.. Information extraction from electronic health records to predict readmission following acute myocardial infarction: does natural language processing using clinical notes improve prediction of readmission?. J Am Heart Assoc. 2022, 11:e024198. 10.1161\/JAHA.121.024198","journal-title":"J Am Heart Assoc"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcmg.2022.07.017","article-title":"Direct risk assessment from myocardial perfusion imaging using explainable deep learning","volume":"16","author":"Singh A","year":"2023","unstructured":"Singh A, Miller RJ, Otaki Y, et al.. Direct risk assessment from myocardial perfusion imaging using explainable deep learning. JACC Cardiovasc Imaging. 2023, 16:209-20. 10.1016\/j.jcmg.2022.07.017","journal-title":"JACC Cardiovasc Imaging"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijcard.2024.131945","article-title":"Artificial intelligence-based quantitative coronary angiography of major vessels using deep-learning","volume":"405","author":"In Kim Y","year":"2024","unstructured":"In Kim Y, Roh JH, Kweon J, et al.. Artificial intelligence-based quantitative coronary angiography of major vessels using deep-learning. Int J Cardiol. 2024, 405:131945. 10.1016\/j.ijcard.2024.131945","journal-title":"Int J Cardiol"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(22)00022-X","article-title":"Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study","volume":"4","author":"Lin A","year":"2022","unstructured":"Lin A, Manral N, McElhinney P, et al.. Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study. Lancet Digit Health. 2022, 4:e256-65. 10.1016\/S2589-7500(22)00022-X","journal-title":"Lancet Digit Health"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.3389\/fphys.2025.1635923","article-title":"Efficacy of artificial intelligence-based FFR technology for coronary CTA stenosis detection in clinical management of coronary artery disease: a systematic review","volume":"16","author":"Liu T","year":"2025","unstructured":"Liu T, Liu M, Aisika A, Wumaier P, Abulizi A, Wang J, Nijiati M. Efficacy of artificial intelligence-based FFR technology for coronary CTA stenosis detection in clinical management of coronary artery disease: a systematic review. Front Physiol. 2025, 16:1635923. 10.3389\/fphys.2025.1635923","journal-title":"Front Physiol"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2024.1398963","article-title":"Diagnostic value of artificial intelligence-assisted CTA for the assessment of atherosclerosis plaque: a systematic review and meta-analysis","volume":"11","author":"Jie P","year":"2024","unstructured":"Jie P, Fan M, Zhang H, et al.. Diagnostic value of artificial intelligence-assisted CTA for the assessment of atherosclerosis plaque: a systematic review and meta-analysis. Front Cardiovasc Med. 2024, 11:1398963. 10.3389\/fcvm.2024.1398963","journal-title":"Front Cardiovasc Med"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcmg.2023.05.020","article-title":"AI-guided quantitative plaque staging predicts long-term cardiovascular outcomes in patients at risk for atherosclerotic CVD","volume":"17","author":"Nurmohamed NS","year":"2024","unstructured":"Nurmohamed NS, Bom MJ, Jukema RA, et al.. AI-guided quantitative plaque staging predicts long-term cardiovascular outcomes in patients at risk for atherosclerotic CVD. JACC Cardiovasc Imaging. 2024, 17:269-80. 10.1016\/j.jcmg.2023.05.020","journal-title":"JACC Cardiovasc Imaging"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCIMAGING.124.016958","article-title":"Patient-specific myocardial infarction risk thresholds from AI-enabled coronary plaque analysis","volume":"17","author":"Miller RJ","year":"2024","unstructured":"Miller RJ, Manral N, Lin A, et al.. Patient-specific myocardial infarction risk thresholds from AI-enabled coronary plaque analysis. Circ Cardiovasc Imaging. 2024, 17:e016958. 10.1161\/CIRCIMAGING.124.016958","journal-title":"Circ Cardiovasc Imaging"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcct.2024.05.232","article-title":"Standards for quantitative assessments by coronary computed tomography angiography (CCTA): an expert consensus document of the Society of Cardiovascular Computed Tomography\u00a0(SCCT)","volume":"18","author":"Nieman K","year":"2024","unstructured":"Nieman K, Garc\u00eda-Garc\u00eda HM, Hideo-Kajita A, et al.. Standards for quantitative assessments by coronary computed tomography angiography (CCTA): an expert consensus document of the Society of Cardiovascular Computed Tomography\u00a0(SCCT). J Cardiovasc Comput Tomogr. 2024, 18:429-43. 10.1016\/j.jcct.2024.05.232","journal-title":"J Cardiovasc Comput Tomogr"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.2020200009","article-title":"Automated inline analysis of myocardial perfusion MRI with deep learning","volume":"2","author":"Xue H","year":"2020","unstructured":"Xue H, Davies RH, Brown LA, et al.. Automated inline analysis of myocardial perfusion MRI with deep learning. Radiol Artif Intell. 2020, 2:e200009. 10.1148\/ryai.2020200009","journal-title":"Radiol Artif Intell"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.122.060137","article-title":"Artificial intelligence for contrast-free MRI: scar assessment in myocardial infarction using deep learning-based virtual native enhancement","volume":"146","author":"Zhang Q","year":"2022","unstructured":"Zhang Q, Burrage MK, Shanmuganathan M, et al.. Artificial intelligence for contrast-free MRI: scar assessment in myocardial infarction using deep learning-based virtual native enhancement. Circulation. 2022, 146:1492-503. 10.1161\/CIRCULATIONAHA.122.060137","journal-title":"Circulation"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1186\/s41747-024-00497-3","article-title":"Automated cardiovascular MR myocardial scar quantification with unsupervised domain adaptation","volume":"8","author":"Crawley R","year":"2024","unstructured":"Crawley R, Amirrajab S, Lustermans D, et al.. Automated cardiovascular MR myocardial scar quantification with unsupervised domain adaptation. Eur Radiol Exp. 2024, 8:93. 10.1186\/s41747-024-00497-3","journal-title":"Eur Radiol Exp"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.107321","article-title":"An accurate and time-efficient deep learning-based system for automated segmentation and reporting of cardiac magnetic resonance-detected ischemic scar","volume":"229","author":"Papetti DM","year":"2023","unstructured":"Papetti DM, Van Abeelen K, Davies R, et al.. An accurate and time-efficient deep learning-based system for automated segmentation and reporting of cardiac magnetic resonance-detected ischemic scar. Comput Methods Programs Biomed. 2023, 229:107321. 10.1016\/j.cmpb.2022.107321","journal-title":"Comput Methods Programs Biomed"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.cvdhj.2021.11.007","article-title":"Anatomically informed deep learning on contrast-enhanced cardiac magnetic resonance imaging for scar segmentation and clinical feature extraction","volume":"3","author":"Popescu DM","year":"2022","unstructured":"Popescu DM, Abramson HG, Yu R, et al.. Anatomically informed deep learning on contrast-enhanced cardiac magnetic resonance imaging for scar segmentation and clinical feature extraction. Cardiovasc Digit Health J. 2022, 3:2-13. 10.1016\/j.cvdhj.2021.11.007","journal-title":"Cardiovasc Digit Health J"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1093\/ehjdh\/ztae002","article-title":"Detection of acute coronary occlusion with a novel mobile electrocardiogram device: a pilot study","volume":"5","author":"Zepeda-Echavarria A","year":"2024","unstructured":"Zepeda-Echavarria A, van de Leur RR, Vessies M, et al.. Detection of acute coronary occlusion with a novel mobile electrocardiogram device: a pilot study. Eur Heart J Digit Health. 2024, 5:183-91. 10.1093\/ehjdh\/ztae002","journal-title":"Eur Heart J Digit Health"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1007\/BF02344885","article-title":"Long-term ST database: a reference for the development and evaluation of automated ischaemia detectors and for the study of the dynamics of myocardial ischaemia","volume":"41","author":"Jager F","year":"2003","unstructured":"Jager F, Taddei A, Moody GB, et al.. Long-term ST database: a reference for the development and evaluation of automated ischaemia detectors and for the study of the dynamics of myocardial ischaemia. Med Biol Eng Comput. 2003, 41:172-82. 10.1007\/BF02344885","journal-title":"Med Biol Eng Comput"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1016\/S0933-3657(01)00100-2","article-title":"An ischemia detection method based on artificial neural networks","volume":"24","author":"Papaloukas C","year":"2002","unstructured":"Papaloukas C, Fotiadis D, Likas A, Michalis LK. An ischemia detection method based on artificial neural networks. Artif Intell Med. 2002, 24:167-78. 10.1016\/S0933-3657(01)00100-2","journal-title":"Artif Intell Med"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1007\/BF02344704","article-title":"Automated detection of transient ST-segment episodes in 24h electrocardiograms","volume":"42","author":"Smrdel A","year":"2004","unstructured":"Smrdel A, Jager F. Automated detection of transient ST-segment episodes in 24h electrocardiograms. Med Biol Eng Comput. 2004, 42:303-11. 10.1007\/BF02344704","journal-title":"Med Biol Eng Comput"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.2196\/40826","article-title":"Motion artifact reduction in electrocardiogram signals through a redundant denoising independent component analysis method for wearable health care monitoring systems: algorithm development and validation","volume":"10","author":"Casta\u00f1o Usuga FA","year":"2022","unstructured":"Casta\u00f1o Usuga FA, Gissel C, Hern\u00e1ndez AM. Motion artifact reduction in electrocardiogram signals through a redundant denoising independent component analysis method for wearable health care monitoring systems: algorithm development and validation. JMIR Med Inform. 2022, 10:e40826. 10.2196\/40826","journal-title":"JMIR Med Inform"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-56595-0","article-title":"ECG signal quality in intermittent long-term dry electrode recordings with controlled motion artifacts","volume":"14","author":"Joutsen A","year":"2024","unstructured":"Joutsen A, C\u00f6mert A, Kaappa E, Vanhatalo K, Riistama J, Vehkaoja A, Eskola H. ECG signal quality in intermittent long-term dry electrode recordings with controlled motion artifacts. Sci Rep. 2024, 14:8882. 10.1038\/s41598-024-56595-0","journal-title":"Sci Rep"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering11030293","article-title":"Wearable 12-lead ECG acquisition using a novel deep learning approach from Frank or EASI leads with clinical validation","volume":"11","author":"Fu F","year":"2024","unstructured":"Fu F, Zhong D, Liu J, et al.. Wearable 12-lead ECG acquisition using a novel deep learning approach from Frank or EASI leads with clinical validation. Bioengineering (Basel). 2024, 11:293. 10.3390\/bioengineering11030293","journal-title":"Bioengineering (Basel)"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.3390\/s24030828","article-title":"Real-time myocardial infarction detection approaches with a microcontroller-based edge-AI device","volume":"24","author":"Gragnaniello M","year":"2024","unstructured":"Gragnaniello M, Borghese A, Marrazzo VR, Maresca L, Breglio G, Irace A, Riccio M. Real-time myocardial infarction detection approaches with a microcontroller-based edge-AI device. Sensors (Basel). 2024, 24:828. 10.3390\/s24030828","journal-title":"Sensors (Basel)"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1049\/htl.2015.0030","article-title":"An energy efficient application specific integrated circuit for electrocardiogram feature detection and its potential for ambulatory cardiovascular disease detection","volume":"3","author":"Jain SK","year":"2016","unstructured":"Jain SK, Bhaumik B. An energy efficient application specific integrated circuit for electrocardiogram feature detection and its potential for ambulatory cardiovascular disease detection. Healthc Technol Lett. 2016, 3:77-84. 10.1049\/htl.2015.0030","journal-title":"Healthc Technol Lett"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.2196\/60465","article-title":"Assessment of the sensitivity of a smartphone app to assist patients in the identification of stroke and myocardial infarction: cross-sectional study","volume":"9","author":"Dhand A","year":"2025","unstructured":"Dhand A, Mangipudi R, Varshney AS, et al.. Assessment of the sensitivity of a smartphone app to assist patients in the identification of stroke and myocardial infarction: cross-sectional study. JMIR Form Res. 2025, 9:e60465. 10.2196\/60465","journal-title":"JMIR Form Res"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(24)00191-2","article-title":"Diagnostic accuracy of a machine learning algorithm using point-of-care high-sensitivity cardiac troponin I for rapid rule-out of myocardial infarction: a retrospective study","volume":"6","author":"Toprak B","year":"2024","unstructured":"Toprak B, Solleder H, Di Carluccio E, et al.. Diagnostic accuracy of a machine learning algorithm using point-of-care high-sensitivity cardiac troponin I for rapid rule-out of myocardial infarction: a retrospective study. Lancet Digit Health. 2024, 6:729-38. 10.1016\/S2589-7500(24)00191-2","journal-title":"Lancet Digit Health"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-023-00956-y","article-title":"Digital health application integrating wearable data and behavioral patterns improves metabolic health","volume":"6","author":"Zahedani AD","year":"2023","unstructured":"Zahedani AD, McLaughlin T, Veluvali A, et al.. Digital health application integrating wearable data and behavioral patterns improves metabolic health. NPJ Digit Med. 2023, 6:216. 10.1038\/s41746-023-00956-y","journal-title":"NPJ Digit Med"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1016\/j.ahjo.2025.100528","article-title":"A multicenter pragmatic implementation study of AI-ECG-based clinical decision support software to identify low LVEF: clinical trial design and methods","volume":"54","author":"Lopez-Jimenez F","year":"2025","unstructured":"Lopez-Jimenez F, Alger HM, Attia ZI, et al.. A multicenter pragmatic implementation study of AI-ECG-based clinical decision support software to identify low LVEF: clinical trial design and methods. Am Heart J Plus. 2025, 54:100528. 10.1016\/j.ahjo.2025.100528","journal-title":"Am Heart J Plus"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocz095","article-title":"Medication safety alert fatigue may be reduced via interaction design and clinical role tailoring: a systematic review","volume":"26","author":"Hussain MI","year":"2019","unstructured":"Hussain MI, Reynolds TL, Zheng K. Medication safety alert fatigue may be reduced via interaction design and clinical role tailoring: a systematic review. J Am Med Inform Assoc. 2019, 26:1141-9. 10.1093\/jamia\/ocz095","journal-title":"J Am Med Inform Assoc"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01340-0","article-title":"Learning from the EHR to implement AI in healthcare","volume":"7","author":"Rose C","year":"2024","unstructured":"Rose C, Chen JH. Learning from the EHR to implement AI in healthcare. NPJ Digit Med. 2024, 7:330. 10.1038\/s41746-024-01340-0","journal-title":"NPJ Digit Med"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1136\/amiajnl-2011-000089","article-title":"Automation bias: a systematic review of frequency, effect mediators, and mitigators","volume":"19","author":"Goddard K","year":"2012","unstructured":"Goddard K, Roudsari A, Wyatt JC. Automation bias: a systematic review of frequency, effect mediators, and mitigators. J Am Med Inform Assoc. 2012, 19:121-7. 10.1136\/amiajnl-2011-000089","journal-title":"J Am Med Inform Assoc"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1111\/eci.14370","article-title":"Applications, challenges and future directions of artificial intelligence in cardio-oncology","volume":"55 Suppl 1","author":"Ravera F","year":"2025","unstructured":"Ravera F, Gilardi N, Ballestrero A, Zoppoli G. Applications, challenges and future directions of artificial intelligence in cardio-oncology. Eur J Clin Invest. 2025, 55 Suppl 1:e14370. 10.1111\/eci.14370","journal-title":"Eur J Clin Invest"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.37349\/ec.2024.00038","article-title":"Emerging technologies and applications in multimodality imaging for ischemic heart disease: current state and future of artificial intelligence","volume":"2","author":"Sekar PKC","year":"2019","unstructured":"Sekar PKC, Veerabathiran R. Emerging technologies and applications in multimodality imaging for ischemic heart disease: current state and future of artificial intelligence. Explor Cardiol. 2019, 2:253-64. 10.37349\/ec.2024.00038","journal-title":"Explor Cardiol"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1007\/s11886-025-02203-0","article-title":"Artificial intelligence in ischemic heart disease prevention","volume":"27","author":"Parsa S","year":"2025","unstructured":"Parsa S, Shah P, Doijad R, Rodriguez F. Artificial intelligence in ischemic heart disease prevention. Curr Cardiol Rep. 2025, 27:44. 10.1007\/s11886-025-02203-0","journal-title":"Curr Cardiol Rep"}],"container-title":["Cureus"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cureus.com\/articles\/422500-the-digital-revolution-in-cardiac-ischemia-artificial-intelligence-ai-enhanced-detection-diagnosis-and-risk-stratification","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T14:40:19Z","timestamp":1761057619000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cureus.com\/articles\/422500-the-digital-revolution-in-cardiac-ischemia-artificial-intelligence-ai-enhanced-detection-diagnosis-and-risk-stratification"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"references-count":67,"URL":"https:\/\/doi.org\/10.7759\/cureus.95059","relation":{},"ISSN":["2168-8184"],"issn-type":[{"value":"2168-8184","type":"print"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}