AI-Augmented Diagnostics: A New Era for Coronary Syndrome Management

AI-Augmented Diagnostics: A New Era for Coronary Syndrome Management | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 30 July 2025 V1 Latest version Share on AI-Augmented Diagnostics: A New Era for Coronary Syndrome Management Author : Fahad Amin 0009-0007-2076-9633 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175391115.54155824/v1 152 views 116 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Acute Coronary Syndrome (ACS), encompassing unstable angina, NSTEMI, and STEMI, remains a leading cause of morbidity and mortality worldwide. Early diagnosis is vital for reducing myocardial injury and improving survival; however, traditional diagnostic methods, including ECG and biomarkers, often fail to detect atypical cases, particularly in high-risk groups. Artificial intelligence (AI) and machine learning (ML) are emerging as transformative tools in cardiovascular diagnostics by analyzing complex datasets and identifying patterns overlooked by conventional approaches. Studies have demonstrated that AI models can achieve superior diagnostic accuracy compared to standard STEMI criteria, with some models, such as the PMCardio Queen of Hearts, detecting LAD occlusion myocardial infarction with 100% sensitivity even when conventional criteria were not met. Additionally, data mining techniques have shown high predictive value in assessing heart disease risk. Despite these advancements, limitations remain, including dataset bias, lack of real-world validation, interpretability concerns, and regulatory challenges. Looking ahead, integrating AI with multimodal data sources, developing explainable models, and conducting large-scale clinical trials are crucial for its safe and effective adoption. With continued advancements, AI has the potential to become an indispensable tool in ACS diagnosis and management, ultimately improving patient outcomes globally. AI-Augmented Diagnostics: A New Era for Coronary Syndrome Management Fahad Amin 1 King Edward Medical Universirty, Lahore, Pakistan [email protected] Abstract: Acute Coronary Syndrome (ACS), encompassing unstable angina, NSTEMI, and STEMI, remains a leading cause of morbidity and mortality worldwide. Early diagnosis is vital for reducing myocardial injury and improving survival; however, traditional diagnostic methods, including ECG and biomarkers, often fail to detect atypical cases, particularly in high-risk groups. Artificial intelligence (AI) and machine learning (ML) are emerging as transformative tools in cardiovascular diagnostics by analyzing complex datasets and identifying patterns overlooked by conventional approaches. Studies have demonstrated that AI models can achieve superior diagnostic accuracy compared to standard STEMI criteria, with some models, such as the PMCardio Queen of Hearts, detecting LAD occlusion myocardial infarction with 100% sensitivity even when conventional criteria were not met. Additionally, data mining techniques have shown high predictive value in assessing heart disease risk. Despite these advancements, limitations remain, including dataset bias, lack of real-world validation, interpretability concerns, and regulatory challenges. Looking ahead, integrating AI with multimodal data sources, developing explainable models, and conducting large-scale clinical trials are crucial for its safe and effective adoption. With continued advancements, AI has the potential to become an indispensable tool in ACS diagnosis and management, ultimately improving patient outcomes globally. Manuscript: To the Editor, Acute Coronary Syndrome (ACS), which includes unstable angina, NSTEMI, and STEMI, results from a sudden reduction in coronary blood flow and remains a major cause of morbidity and mortality worldwide [1]. Early diagnosis is crucial and enables rapid treatment, thereby minimizing myocardial injury and increasing mortality. But standard diagnostic methods like ECG and cardiac markers can miss ambiguous cases, especially in elderly, diabetic, or female patients, causing delays in management. Proper usage of suitable treatments depends on the exact distinction of ACS subtypes. Despite imaging and risk score developments, major diagnostic limits remain [2]; hence, more sophisticated diagnostic methods are needed. By analyzing data from medical records, imaging, and monitoring instruments, artificial intelligence (AI) and machine learning (ML) have revolutionized medical diagnosis. They are able to detect uncommon and rare markers and patterns missed by the conventional methods. Early discovery, risk exemptions, and treatment planning across domains, including cardiology, oncology, neurology, and dermatology, have all been shown to be accurate using these techniques. In cardiology, ML-based tools such as CT-fractional flow reserve (CT-effect) and Support Vector Machines (SVMs) have achieved diagnostic accuracy above 90% in detecting coronary heart disease and arrhythmias [3]. By activating previous and more precise interventions, the AI has the potential to improve patient results to a large extent while improving the efficiency of the health care system. AI has shown great promise in the early diagnosis of ACS, especially in the setting of emergency situations. An artificial intelligence model outperformed conventional STEMI criteria in a study of 217 patients with a sensitivity of 86.5%, a specificity of 82.2%, and an AUC of 0.84 [4]. Further evidence from the DOMIARIGATO substudy showed that the PMCardio Queen of Hearts model identified LAD occlusion myocardial infarction with 100% sensitivity on the first ECG, even when STEMI criteria were not met [5]. Moreover, studies by Ahuja et al. and Xing et al. have found great accuracy of data mining methods in forecasting heart disease outcomes [6, 7]. Together, these results highlight the capacity of artificial intelligence to better early detection, risk assessment, and prompt interventions, so improving both patient outcomes and decision-making. Still, AI and ML have several drawbacks even with their potential. Models trained on limited and specific datasets may not generalize across varied populations [8], and their performance is strongly determined by the quality and completeness of the input data. Much research showcasing the effectiveness of artificial intelligence is retrospective and carried out in controlled circumstances with little validation in actual world environments. The "black box" quality of artificial intelligence algorithms lowers interpretability; therefore, doctors are reluctant to depend only on its results [9]. Integration into current clinical procedures is also challenged by technical, economic, and legal barriers. Overreliance on artificial intelligence without medical supervision may raise the chance of misdiagnosis. Looking forward to the future, AI and ML have the ability to revolutionize ACS diagnosis and treatment by spotting faint patterns and combining several data sources, including imaging, genomics, and real-time monitoring. Future projects should aim at bigger and more robust trials, creating open AI models, and setting up strict ethical and legislative norms. These developments could turn artificial intelligence into an essential tool in cardiovascular treatment, therefore enhancing diagnostic accuracy and patient outcomes globally. References: 1. Singh, A., Museedi, A. S., & Grossman, S. A. (2023, July 10). Acute coronary syndrome . StatPearls - NCBI Bookshelf. https://www.ncbi.nlm.nih.gov/books/NBK459157/ 2. Kwok, C. S., Bennett, S., Azam, Z., Welsh, V., Potluri, R., Loke, Y. K., & Mallen, C. D. (2021). Misdiagnosis of Acute myocardial Infarction: A Systematic Review of the literature. Critical Pathways in Cardiology a Journal of Evidence-Based Medicine , 20 (3), 155–162. https://doi.org/10.1097/hpc.0000000000000256 3. Nia, N. G., Kaplanoglu, E., & Nasab, A. (2023). Evaluation of artificial intelligence techniques in disease diagnosis and prediction. Discover Artificial Intelligence , 3 (1). https://doi.org/10.1007/s44163-023-00049-5 4. Choi JWH, Torelli V, Silverman A, Diaz SS, Kong D, Vaish E, Katic L, Nagourney A, Khan Z, Robbins L, Pinney S, Barman N, Farhan S. AI-enhanced recognition of occlusions in acute coronary syndrome (AERO-ACS): a retrospective study. Coron Artery Dis. 2025 Jul 25. doi: 10.1097/MCA.0000000000001555. Epub ahead of print. PMID: 40705383. 5. Meyers HP, Sharkey SW, Herman R, de Alencar JN, Shroff GR, Frick WH, Smith SW. Failure of standard contemporary ST-elevation myocardial infarction electrocardiogram criteria to reliably identify acute occlusion of the left anterior descending coronary artery. Eur Heart J Acute Cardiovasc Care. 2025 Jul 28:zuaf037. doi: 10.1093/ehjacc/zuaf037. Epub ahead of print. PMID: 40717627. 6. Masih, N., & Ahuja, S. (2018). Prediction of heart diseases using data mining techniques. International Journal of Big Data and Analytics in Healthcare , 3 (2), 1–9. https://doi.org/10.4018/ijbdah.2018070101 7. Xing, Y.W., Wang, J., Zhao, Z.H. and Gao, Y.H. (2007) Combination Data Mining Methods with New Medical Data to Predicting Outcome of Coronary Heart Disease. Convergence Information Technology, Gwangju, 21-23 November 2007, 868-872. - References - Scientific Research Publishing . (n.d.). https://www.scirp.org/reference/referencespapers?referenceid=3401225 8. Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., Ourselin, S., Sheller, M., Summers, R. M., Trask, A., Xu, D., Baust, M., & Cardoso, M. J. (2020). The future of digital health with federated learning. Npj Digital Medicine , 3 (1). https://doi.org/10.1038/s41746-020-00323-1 9. Chen, E. (2025). Ethical implications of AI in modern clinical diagnosis. @WalshMedical . https://doi.org/10.35248/2155-9627.25.16.528 Information & Authors Information Version history V1 Version 1 30 July 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Authors Affiliations Fahad Amin 0009-0007-2076-9633 [email protected] King Edward Medical University View all articles by this author Metrics & Citations Metrics Article Usage 152 views 116 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Fahad Amin. AI-Augmented Diagnostics: A New Era for Coronary Syndrome Management. Authorea . 30 July 2025. DOI: https://doi.org/10.22541/au.175391115.54155824/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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