Integrating High-Sensitivity Troponin and Artificial Intelligence for Rapid Myocardial Infarction Detection: A Clinical Review

Abstract

Myocardial infarction (MI) remains a critical condition in which rapid and reliable diagnosis is essential for reducing complications and improving survival. High-sensitivity cardiac troponin (hs-cTn) assays have significantly advanced early MI detection by identifying very small rises in troponin levels within hours and enabling the use of accelerated diagnostic pathways such as the ESC 0/1-hour algorithm. Alongside this, artificial intelligence (AI) has emerged as a powerful tool capable of integrating ECG data, troponin trends, clinical features, and hemodynamic variables to refine diagnostic judgments. The combined application of hs-cTn and AI has demonstrated superior accuracy in ruling in or ruling out MI, easing emergency department burden, preventing unnecessary admissions, and promoting faster initiation of reperfusion therapy. AI-based systems also enhance the ability to distinguish MI subtypes and detect early ischemic changes even before measurable biomarker elevation. Despite limitations like data bias and model opacity, the hs-cTn–AI integration holds strong potential to advance acute cardiac care and improve patient outcomes.

Keywords

Introduction

Myocardial infarction (MI) is still a life-threatening emergency, and survival rates, revascularization success, and infarct size are all strongly impacted by early identification. Chest discomfort accounts for up to 10–20% of acute presentations in the majority of emergency rooms (EDs), however only a small percentage of these cases actually indicate MI. In many cases, this diagnostic uncertainty leads to overcrowding, needless admissions, and delayed decision-making. In the past, doctors were obliged to depend mostly on repeated testing and ECG interpretation because conventional troponin assays frequently fail to detect injury within the first three to six hours of symptom onset. Because high-sensitivity cardiac troponin (hs-cTn) assays may detect minute elevations within 1-2 hours of myocardial damage and enable quantifiable troponin readings in over 50% of healthy patients, they have revolutionized early MI detection ^(1,3). Clinicians can reliably rule out MI in a significant percentage of low-risk patients thanks to these assays' support for faster diagnostic methods like the ESC 0/1-hour and 0/2-hour algorithms.
Concurrent developments in artificial intelligence (AI) open up new possibilities for combining clinical, biochemical, and ECG data to enhance diagnosis accuracy and improve risk stratification. AI-based models are able to identify minor patterns in ECG anomalies, troponin kinetics, and patient features that may be difficult for physicians to visually understand ^(4,6). For instance, it has been demonstrated that adding troponin delta changes to machine learning models improves the ability to distinguish between type 1 MI and non-ischemic myocardial damage, a typical diagnostic difficulty in emergency department practice ⁽⁸⁾. AI may ease ED congestion, cut door-to-needle times, and enable early discharge of low-risk patients by facilitating quick triage judgments. All things considered, integrating hs-cTn tests with AI-enabled clinical decision support systems is a potential approach to improve patient outcomes, workflow efficiency, and diagnostic accuracy in acute coronary syndromes.

HIGH-SENSITIVITY TROPONIN: BIOCHEMICAL AND CLINICAL RELEVANCE

Clinicians can detect myocardial necrosis much earlier thanks to high-sensitivity troponin assays, which detect circulating cardiac troponin I or T at quantities substantially below the threshold of standard testing. These assays provide coefficients of variation <10% at the 99th-percentile threshold ⁽²⁾ and can analytically measure troponin quantities beyond the limit of detection in at least 50% of healthy individuals ⁽³⁾. Clinically speaking, hs-cTn tests improve the early diagnosis of MI, especially non-ST elevation myocardial infarction (NSTEMI), for which ECG results are frequently indeterminate.

The ESC 0/1-hour algorithm stratifies patients into rule-out, rule-in, or observe categories based on baseline hs-cTn levels and absolute delta changes at one hour. This algorithm has been confirmed across several cohort studies. Clinicians may reliably rule out MI in 50–60% of ED patients within 60 minutes thanks to these expedited approaches, reducing needless hospital stays ⁽³⁾. The accuracy of the diagnosis is further improved by combining the hs-cTn data with clinical risk scores and symptom start time. Crucially, non-ischemic troponin elevations can be caused by a variety of diseases, including heart failure, renal dysfunction, sepsis, tachyarrhythmias, and myocarditis ^(1,2). Therefore, mild troponin elevations should be interpreted cautiously. For physicians who rely on serial measures, clinical judgment, and ancillary testing, this diagnostic overlap frequently presents difficulties.

Troponin release's kinetic pattern is crucial for clinical purposes. Instead of a single static elevation, a rising or decreasing pattern strongly signals acute myocardial damage. High-sensitivity assays increase physician confidence by identifying these dynamic changes earlier. For AI-based modeling systems, which are particularly good at examining temporal biomarker patterns, this kinetic behavior offers the perfect dataset ⁽⁸⁾. Thus, hs-cTn offers a data-rich platform for computational models that assist in clinical decision-making in addition to improving biochemical detection.

ARTIFICIAL INTELLIGENCE IN CARDIOVASCULAR DIAGNOSTICS: CLINICAL ROLE

The use of artificial intelligence in cardiovascular practice is growing, particularly in the emergency assessment of chest discomfort. Multidimensional information including ECG waveforms, serial troponin levels, hemodynamic parameters, comorbidities, and clinical symptoms can be processed by AI models, especially machine learning (ML) and deep learning (DL) systems. These models aid in the discovery of hidden patterns in clinical situations that physicians might overlook due to time constraints. For instance, AI-enabled ECG interpretation systems have shown sensitivity in identifying minute ischemia changes that is on par with that of skilled cardiologists ^(4,7). This is especially important in cases of atypical MI presentation, where there may be limited ECG abnormalities or ambiguous symptoms. Risk stratification is one of the most important applications of AI in medicine. Research shows that when it comes to forecasting adverse cardiac events, AI-enhanced decision support tools perform better than conventional scoring systems like GRACE and TIMI ⁽⁶⁾. Additionally, AI can categorize MI phenotypes, such as differentiating between type 1 and type 2 MI or chronic myocardial damage, assisting medical professionals in choosing the best course of treatment ⁽⁸⁾. When troponin kinetics or clinical indicators show an increase in the likelihood of MI, AI models connected with electronic health records in the emergency department provide real-time decision support.

AI is showing promise in prehospital environments as well. According to a number of studies, AI applied to ambulance ECGs can help early activation of cardiac catheterization labs by predicting subsequent troponin rise prior to hospital arrival ^(7,9). Clinicians must, however, continue to be mindful of issues including overfitting, data bias, and opaque model interpretation. For this reason, incorporating explainable AI (XAI) systems is essential to preserving clinician trust and guaranteeing secure decision-making.

SYNERGISTIC USE OF HS-CTN + AI: IMPACT IN CLINICAL PRACTICE

The combination of hs-cTn assays and AI-enabled analytical tools provides a potent diagnostic tool with immediate clinical applications. AI improves diagnostic interpretation by analyzing patterns and trajectories within repeated troponin data; troponin alone offers biochemical evidence of myocardial injury but lacks specificity for ischemia. The capacity to distinguish between acute MI and chronic elevation, which is particularly difficult in individuals with renal impairment or chronic structural heart disease, has been markedly improved by models that include troponin delta values ^(8,10).

Rapid triage is supported in real ED workflows by hs-cTn + AI systems. AI-enhanced versions of the ESC 0/1-hour algorithm, for instance, can automatically categorize patients into rule-in or rule-out groups, reducing decision fatigue and clinical stress. AI-supported troponin interpretation has been shown in studies to have diagnostic sensitivities greater than 95%, which lowers the chance of missing MI ^(6,8,11). Clinically, this means shorter observation periods, safer early discharge for low-risk patients, and faster activation of reperfusion pathways for high-risk patients. The combination is particularly helpful in cases that are challenging to diagnose, such as elderly individuals, women with unusual presentations, or diabetics with silent ischemia. Even before troponin surpasses diagnostic criteria, AI algorithms trained on vast datasets can identify patterns suggestive of developing MI. This capacity for prediction could enable earlier intervention and better results. ⁽¹²⁾

Key Clinical Benefits of hs-cTn + AI

Table1.1: Key Clinical Benefits of hs-cTn + AI

CLINICAL APPLICATIONS AND EVIDENCE

The combination of hs-cTn with AI for MI detection is strongly supported by real-world clinical evidence. When compared to conventional evaluation techniques, machine learning models that incorporated hs-cTn values, ECG findings, symptoms, and demographics greatly improved early MI detection in large multicenter studies involving ED chest pain groups ⁽⁸⁾. In a study with over 10,000 patients, it was shown that AI-augmented troponin algorithms achieved sensitivities above 98% and permitted safe rule-out of MI in over 50% of patients within an hour ⁽¹³⁾. AI-enhanced ECG analysis is also a critical diagnostic tool in practice. Deep learning models trained on millions of ECGs have shown the capacity to identify ischemia signatures that substantially correlate with troponin elevation, even while standard ECG interpretation is still restricted in early NSTEMI ⁽⁴⁾. Even before biochemical proof, this predictive power allows for the early identification of at-risk patients. AI-driven ECG interpretation is being used by a number of emergency medical systems in prehospital treatment to predict troponin elevation and speed up cath lab activation ⁽¹⁴⁾. Differentiating between type 1 MI (plaque rupture) and type 2 MI (supply-demand mismatch), which is frequently difficult for clinicians, is another important therapeutic use. AI algorithms that combine hemodynamic and clinical data with serial hs-cTn readings have shown increased accuracy in phenotypic classification, directing therapeutic choices such antithrombotic medication and angiography referral ^(15,16). It has also been demonstrated that AI-driven troponin interpretation lowers overall healthcare expenses and the number of needless admissions to observation units. Crucially, research indicates that early-discharged patients do not have a higher risk of missing MI or unfavorable cardiac outcomes ⁽¹⁶⁾.

CHALLENGES AND LIMITATIONS

Implementing combination hs-cTn + AI systems is difficult despite significant clinical benefits. The lack of specificity of hs-cTn is a significant clinical problem, as it may increase in non-ischemic situations including acute sepsis, myocarditis, or renal failure ^(1,2). False positives and needless invasive procedures could result from AI models that were trained on incomplete or biased datasets misclassifying such situations. To get dependable results, AI systems need big, varied, high-quality datasets. Diagnostic bias may arise if patients with comorbidities, women, or the elderly are underrepresented in training data. Clinically, this may result in disparities in diagnostic precision among patient populations, which raises moral questions ⁽¹⁷⁾. Therefore, it is crucial to ensure external evaluation and diversity data inclusion. Another drawback is interpretability. Deep learning algorithms frequently operate as "black boxes," making predictions without providing a clear justification. Adoption may be hampered and doctor trust diminished. In order to assist doctors, comprehend the logic underlying AI judgments, explainable AI (XAI) techniques are becoming more and more crucial. Integration into electronic health records, compatibility between hospital systems, and teaching physicians how to properly use AI tools are examples of operational barriers. Infrastructure and cost issues may arise in environments with limited resources. And lastly, regulatory supervision is changing. Deployment of AI diagnostic tools is complicated by the necessity for strong clinical validation and possible FDA or EMA approval ⁽¹⁸⁾.

FUTURE PROSPECTS

It is anticipated that future clinical practice would shift toward fully integrated decision-support ecosystems that integrate cloud-based AI, continuous biosensors, point-of-care hs-cTn testing, and real-time clinical triage. Mobile AI apps could be combined with point-of-care hs-cTn devices that can provide quick results at the bedside or in ambulances to speed up field triage and possibly enable MI diagnosis even before ED arrival. Federated learning developments will improve generalizability, lessen bias, and enable AI models to be trained across many hospital networks without jeopardizing patient privacy ^(19,20). In the future, wearable technology that evaluates hemodynamics and ECG may be able to identify ischemia in real time and send out AI-driven alarms for prompt medical attention. By combining imaging, genetics, and longitudinal troponin data to forecast individual risk trajectories and enable proactive preventative efforts, AI may assist clinically customize MI care. To minimize physician decision-making variability and maximize resource utilization, hospitals may implement standardized AI-enabled troponin pathways.

CONCLUSION

The combination of artificial intelligence (AI) and high-sensitivity cardiac troponin (hs-cTn) assays is a revolutionary development in the quick and precise identification of myocardial infarction. Early MI diagnosis has already been transformed by hs-cTn, which supports expedited protocols like the ESC 0/1-hour methodology and allows for accurate detection of minimum myocardial infarction. Diagnostic accuracy significantly increases when paired with AI-driven analytical models that can process troponin dynamics, ECG patterns, and clinical factors, especially in complicated or unclear presentations. Research shows that these combination approaches improve rule-in and rule-out accuracy, decrease overcrowding in emergency rooms, cut down on needless admissions, and speed up the start of reperfusion treatments. Furthermore, AI-enabled interpretation helps distinguish between acute and chronic cardiac damage more accurately, leading to better clinical decision-making. The continued development of explainable AI, federated learning, and point-of-care biosensors promises to further deepen this synergy, despite obstacles including data bias, interpretability, and implementation difficulties. When combined, hs-cTn and AI provide a potent path toward bettering patient outcomes, workflow effectiveness, and the state of acute cardiac care going forward.

REFERENCES

1. Thygesen, K. (2018). What’s new in the Fourth Universal Definition of Myocardial infarction? European Heart Journal, 39(42), 3757–3758. https://doi.org/10.1093/eurheartj/ehy655
2. Chapman, A. R., Lee, K. K., McAllister, D. A., Cullen, L., Greenslade, J. H., Parsonage, W., Worster, A., Kavsak, P. A., Blankenberg, S., Neumann, J., Sörensen, N. A., Westermann, D., Buijs, M. M., Verdel, G. J. E., Pickering, J. W., Than, M. P., Twerenbold, R., Badertscher, P., Sabti, Z., & Mueller, C. (2017). Association of High-Sensitivity Cardiac Troponin I Concentration With Cardiac Outcomes in Patients With Suspected Acute Coronary Syndrome. JAMA, 318(19), 1913–1924. https://doi.org/10.1001/jama.2017.17488
3. Twerenbold, R., Neumann, J. T., Sörensen, N. A., Ojeda, F., Karakas, M., Boeddinghaus, J., Nestelberger, T., Badertscher, P., Rubini Giménez, M., Puelacher, C., Wildi, K., Kozhuharov, N., Breitenbuecher, D., Biskup, E., du Fay de Lavallaz, J., Flores, D., Wussler, D., Miró, Ò., Martín Sánchez, F. J., & Morawiec, B. (2018). Prospective Validation of the 0/1-h Algorithm for Early Diagnosis of Myocardial Infarction. Journal of the American College of Cardiology, 72(6), 620–632. https://doi.org/10.1016/j.jacc.2018.05.040
4. Kavsak, P. A. (2020). Approaching 2020 acuity for high-sensitivity cardiac troponin assays in Clinical Biochemistry. Clinical Biochemistry, 78, 1–3. https://doi.org/10.1016/j.clinbiochem.2020.01.007
5. Byrne, R. A., Rossello, X., Coughlan, J. J., Barbato, E., Berry, C., Chieffo, A., Claeys, M. J., Dan, G.-A., Dweck, M. R., Galbraith, M., Gilard, M., Hinterbuchner, L., Jankowska, E. A., Jüni, P., Kimura, T., Vijay Kunadian, Leosdottir, M., Lorusso, R., Roberto F.E. Pedretti, & Rigopoulos, A. (2023). 2023 ESC Guidelines for the management of acute coronary syndromes. European Heart Journal, 44(38). https://doi.org/10.1093/eurheartj/ehad191
6. Subramani, S., Varshney, N., M. Vijay Anand, Elahi, M., Lamya Ahmed Al-Keridis, Tarun Kumar Upadhyay, Nawaf Alshammari, Saeed, M., Subramanian, K., K. Anbarasu, & K. Rohini. (2023). Cardiovascular diseases prediction by machine learning incorporation with deep learning. Frontiers in Medicine, 10. https://doi.org/10.3389/fmed.2023.1150933
7.  Adedinsewo, D., Carter, R. E., Attia, Z., Johnson, P., Kashou, A. H., Dugan, J. L., Albus, M., Sheele, J. M., Bellolio, F., Friedman, P. A., Lopez-Jimenez, F., & Noseworthy, P. A. (2020). Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction Presenting to the Emergency Department With Dyspnea. Circulation: Arrhythmia and Electrophysiology, 13(8). https://doi.org/10.1161/circep.120.008437
8. Attia, Z. I., Noseworthy, P. A., Lopez-Jimenez, F., Asirvatham, S. J., Deshmukh, A. J., Gersh, B. J., Carter, R. E., Yao, X., Rabinstein, A. A., Erickson, B. J., Kapa, S., & Friedman, P. A. (2019). An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. The Lancet, 394(10201), 861–867. https://doi.org/10.1016/s0140-6736(19)31721-0
9. Apple, F. S., Smith, S. W., Greenslade, J. H., Sandoval, Y., Parsonage, W., Ranasinghe, I., Gaikwad, N., Schulz, K., Stephensen, L., Schmidt, C. W., Okeson, B., Cullen, L., Brownlee, E., Fincher, G., Hall, E., Hancock, R., Gangathimmaiah, V., Hamilton-Craig, C., Hobbins-King, A., & Keijzers, G. (2022). Single High-Sensitivity Point-of-Care Whole-Blood Cardiac Troponin I Measurement to Rule Out Acute Myocardial Infarction at Low Risk. Circulation, 146(25), 1918–1929. https://doi.org/10.1161/circulationaha.122.061148
10. Bularga, A., Lee, K. K., Stewart, S., Ferry, A. V., Chapman, A. R., Marshall, L., Strachan, F. E., Cruickshank, A., Maguire, D., Berry, C., Findlay, I., Shah, A. S. V., Newby, D. E., Mills, N. L., & Anand, A. (2019). High-Sensitivity Troponin and the Application of Risk Stratification Thresholds in Patients With Suspected Acute Coronary Syndrome. Circulation, 140(19), 1557–1568. https://doi.org/10.1161/circulationaha.119.042866
11. Sataranatarajan, K. (2025). A-020 Reassessing high-sensitivity cardiac troponin baseline cutoff for early rule-in of acute myocardial infarction in the emergency department. Clinical Chemistry, 71(Supplement_1). https://doi.org/10.1093/clinchem/hvaf086.020
12.  Improving the diagnosis of myocardial infarction with machine learning. (2023). Nature Medicine, 29(5), 1070–1071. https://doi.org/10.1038/s41591-023-02331-6
13. Steffen, J., Stocker, A., Scherer, C., Haum, M., Fischer, J., Doldi, P. M., Theiss, H., Braun, D., Konstantinos Rizas, Sven Peterß, Jörg Hausleiter, Steffen Massberg, Orban, M., & Deseive, S. (2022). Emergency transcatheter aortic valve implantation for acute heart failure due to severe aortic stenosis in critically ill patients with or without cardiogenic shock. European Heart Journal. Acute Cardiovascular Care, 11(12), 877–886. https://doi.org/10.1093/ehjacc/zuac131
14.  Stelzle, D., Shah, A., Denvir, M., Anand, A., Strachan, F., Chapman, A., Mills, N., & McAllister, D. (2016). HIGH-SENSITIVITY CARDIAC TROPONIN I AND RISK OF HOSPITALISATION WITH HEART FAILURE IN PATIENTS WITH SUSPECTED ACUTE CORONARY SYNDROME: A COHORT STUDY. Journal of the American College of Cardiology, 67(13), 1327. https://doi.org/10.1016/s0735-1097(16)31328-6
15. Siontis, K. C., Noseworthy, P. A., Attia, Z. I., & Friedman, P. A. (2021). Artificial intelligence-enhanced electrocardiography in cardiovascular disease management. Nature Reviews Cardiology, 18(7), 465–478. https://doi.org/10.1038/s41569-020-00503-2
16.  Ghansah, S. (2024). Cardiovascular Disease Risk Prediction Using Machine Learning. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4726955
17.  McRae, A. D., O’Rielly, C. M., & Lang, E. (2022). High-Sensitivity Cardiac Troponins and Clinical Decision Making in Caring for Patients With Chest Pain. 175(1), 137–138. https://doi.org/10.7326/m21-4342
18. Kaura, A., Sterne, J. A. C., Trickey, A., Abbott, S., Mulla, A., Glampson, B., Panoulas, V., Davies, J., Woods, K., Omigie, J., Shah, A. D., Channon, K. M., Weber, J. N., Thursz, M. R., Elliott, P., Hemingway, H., Williams, B., Asselbergs, F. W., O’Sullivan, M., & Lord, G. M. (2020). Invasive versus non-invasive management of older patients with non-ST elevation myocardial infarction (SENIOR-NSTEMI): a cohort study based on routine clinical data. The Lancet, 396(10251), 623–634. https://doi.org/10.1016/S0140-6736(20)30930-2
19. Saleem, W., Hassan, I., Iqbal, F., & Saleem, F. (2023). Role of Troponin-I in Predicting Length of ICU Stay in Post-Cardiac Surgery Patients. Pakistan Journal of Health Sciences, 19–23. https://doi.org/10.54393/pjhs.v4i04.646
20. ‌Ghansah, S. (2024). Cardiovascular Disease Risk Prediction Using Machine Learning. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4726955