EKG Analizi ve Kardiyak Acillerde Yapay Zeka

Kawecki, D., Gierlotka, M., Morawiec, B., Hawranek, M., Tajstra, M., Skrzypek, M., Wojakowski, W., Polonski, L., Nowalany-Kozielska, E., & Gąsior, M. (2017). Direct Admission Versus Interhospital Transfer for Primary Percutaneous Coronary Intervention in ST-Segment Elevation Myocardial Infarction.. JACC. Cardiovascular interventions, 10 5, 438-447 . https://doi.org/10.1016/j.jcin.2016.11.028.

Lyu, X., Rani, S., Manimurugan, S., Maple, C., & Feng, Y. (2025). A Deep Neuro-Fuzzy Method for ECG Big Data Analysis via Exploring Multimodal Feature Fusion. IEEE Transactions on Fuzzy Systems, 33, 444-456. https://doi.org/10.1109/TFUZZ.2024.3416217.

Elias, P., Poterucha, T., Rajaram, V., Moller, L., Rodriguez, V., Bhave, S., Hahn, R., Tison, G., Abreau, S., Barrios, J., Torres, J., Hughes, J., Perez, M., Finer, J., Kodali, S., Khalique, O., Hamid, N., Schwartz, A., Homma, S., Kumaraiah, D., Cohen, D., Maurer, M., Einstein, A., Nazif, T., Leon, M., & Perotte, A. (2022). Deep Learning Electrocardiographic Analysis for Detection of Left-Sided Valvular Heart Disease.. Journal of the American College of Cardiology, 80 6, 613-626 . https://doi.org/10.1016/j.jacc.2022.05.029.

Jing, L., Finer, J., Hartzel, D., Kelsey, C., Rocha, D., Ruhl, J., Volodarskiy, A., Beecy, A., Haggerty, C., Poterucha, T., & Elias, P. (2023). Abstract 14647: EchoNext: An ECG-Based Deep Learning Model to Detect Structural Heart Disease. Circulation. https://doi.org/10.1161/circ.148.suppl_1.14647.

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(20):1917-1929. doi:10.1093/eurheartj/ehaf004

Su, Y. T., Chen, S. J., Lin, C., Lin, C. S., & Hu, H. F. (2025). Prognostic Significance of AI-Enhanced ECG for Emergency Department Patients. Diagnostics, 15(15), 1874.

Roth, G., Mensah, G., Johnson, C., Addolorato, G., Ammirati, E., Baddour, L., Barengo, N., Beaton, A., Benjamin, E., Benziger, C., Bonny, A., Brauer, M., Brodmann, M., Cahill, T., Carapetis, J., Catapano, A., Chugh, S., Cooper, L., Coresh, J., Criqui, M., Decleene, N., Eagle, K., Emmons-Bell, S., Feigin, V., Fernandez-Solà, J., Fowkes, G., Gakidou, E., Grundy, S., He, F., Howard, G., Hu, F., Inker, L., Karthikeyan, G., Kassebaum, N., Koroshetz, W., Lavie, C., Lloyd-Jones, D., Lu, H., Mirijello, A., Temesgen, A., Mokdad, A., Moran, A., Muntner, P., Narula, J., Neal, B., Ntsekhe, M., De Oliveira, G., Otto, C., Owolabi, M., Pratt, M., Rajagopalan, S., Reitsma, M., Ribeiro, A., Rigotti, N., Rodgers, A., Sable, C., Shakil, S., Sliwa-Hahnle, K., Stark, B., Sundström, J., Timpel, P., Tleyjeh, I., Valgimigli, M., Vos, T., Whelton, P., Yacoub, M., Zuhlke, L., Murray, C., & Fuster, V. (2020). Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019. Journal of the American College of Cardiology, 76, 2982 - 3021. https://doi.org/10.1016/j.jacc.2020.11.010.

Di Cesare, M., Perel, P., Taylor, S., Kabudula, C., Bixby, H., Gaziano, T., McGhie, D., Mwangi, J., Pervan, B., Narula, J., Piñeiro, D., & Pinto, F. (2024). The Heart of the World. Global Heart, 19. https://doi.org/10.5334/gh.1288.

Li, Z., Lin, L., Wu, H., Yan, L., Wang, H., Yang, H., & Li, H. (2021). Global, Regional, and National Death, and Disability-Adjusted Life-Years (DALYs) for Cardiovascular Disease in 2017 and Trends and Risk Analysis From 1990 to 2017 Using the Global Burden of Disease Study and Implications for Prevention. Frontiers in Public Health, 9. https://doi.org/10.3389/fpubh.2021.559751.

Promes SB, Gemme S, Westafer L, Wolf SJ, Diercks DB. Use of high-sensitivity cardiac troponin in the emergency department: A policy resource and education paper (PREP) from the American College of Emergency Physicians. J Am Coll Emerg Physicians Open. 2023 Jul 6;4(4):e12999. doi: 10.1002/emp2.12999. PMID: 37426553; PMCID: PMC10324464.

Casarin, C., Pirot, AS., Gregoire, C. et al. Improving the performance of a triage scale for chest pain patients admitted to emergency departments: combining cardiovascular risk factors and electrocardiogram. BMC Emerg Med 22, 118 (2022). https://doi.org/10.1186/s12873-022-00680-y

Evangeline Loh, Jancy Chee, Tanushri Roy, Wilson Tam, Role of rapid 12-lead electrocardiogram in triage initiatives for ST-elevation myocardial infarction patients self-presenting in emergency departments: a systematic review and meta-analysis, European Journal of Cardiovascular Nursing, Volume 24, Issue 6, August 2025, Pages 841–857, https://doi.org/10.1093/eurjcn/zvaf023

Sanjay, M.; Kurien, Anju Susan; Abraham, Merin Hanna; Speedie, Abraham1,. The Utility of an Electrocardiogram in High-, Intermediate-, and Low-Risk Patients Presenting with Chest Pain to Emergency Department. Current Medical Issues 21(1):p 44-49, Jan–Mar 2023. | DOI: 10.4103/cmi.cmi_102_22

Perrichot, A., Vaittinada Ayar, P., Taboulet, P. et al. Assessment of real-time electrocardiogram effects on interpretation quality by emergency physicians. BMC Med Educ 23, 677 (2023). https://doi.org/10.1186/s12909-023-04670-x

Cook DA, Oh S, Pusic MV. Accuracy of Physicians’ Electrocardiogram Interpretations: A Systematic Review and Meta-analysis. JAMA Intern Med. 2020;180(11):1461–1471. doi:10.1001/jamainternmed.2020.3989

Siontis, K., Noseworthy, P., Attia, Z., & Friedman, P. (2021). Artificial intelligence-enhanced electrocardiography in cardiovascular disease management. Nature Reviews. Cardiology, 18, 465 - 478. https://doi.org/10.1038/s41569-020-00503-2.

Attia, Z., Harmon, D., Behr, E., & Friedman, P. (2021). Application of artificial intelligence to the electrocardiogram. European Heart Journal. https://doi.org/10.1093/eurheartj/ehab649.

Martínez‐Sellés, M., & Marina-Breysse, M. (2023). Current and Future Use of Artificial Intelligence in Electrocardiography. Journal of Cardiovascular Development and Disease, 10. https://doi.org/10.3390/jcdd10040175.

Bishop, A., Nehme, Z., Nanayakkara, S., Anderson, D., Stub, D., & Meadley, B. (2024). Artificial neural networks for ECG interpretation in acute coronary syndrome: A scoping review.. The American journal of emergency medicine, 83, 1-8 . https://doi.org/10.1016/j.ajem.2024.06.026.

Surapaneni, K. (2025). Artificial intelligence-based novel wearables for noninvasive point-of-care assessment of high sensitivity cardiac troponins in patients with acute coronary syndrome. International Journal of Medical Biochemistry. https://doi.org/10.14744/ijmb.2024.55707.

Nie, S., Zhang, S., Zhao, Y., Li, X., Xu, H., Wang, Y., Wang, X., & Zhu, M. (2024). Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management.. Advances in therapy. https://doi.org/10.1007/s12325-024-03060-z.

Choi, J., Kim, J., Spaccarotella, C., Esposito, G., Oh, I., Cho, Y., & Indolfi, C. (2024). Smartwatch ECG and artificial intelligence in detecting acute coronary syndrome compared to traditional 12-lead ECG. International Journal of Cardiology. Heart & Vasculature, 56. https://doi.org/10.1016/j.ijcha.2024.101573.

Hannun AY, Rajpurkar P, Haghpanahi M, et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med. 2019;25(1):65-69. doi:10.1038/s41591-018-0268-3

Attia, Z. I., Noseworthy, P. A., Lopez-Jimenez, F., Asirvatham, S. J., Deshmukh, A. J., Gersh, B. J., ... & 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.

Attia, Z. I., Kapa, S., Lopez-Jimenez, F., McKie, P. M., Ladewig, D. J., Satam, G., ... & Friedman, P. A. (2019). Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram. Nature medicine, 25(1), 70-74.