AI-Enhanced Interpretation of Cardiac Troponins: Toward Predictive Precision in Myocardial Injury

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Notice

nThis is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.n

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Year : 2025 [if 2224 equals=””]07/10/2025 at 3:08 PM[/if 2224] | [if 1553 equals=””] Volume : 15 [else] Volume : 15[/if 1553] | [if 424 equals=”Regular Issue”]Issue : [/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03 | Page :

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    Adnan shams, Bushra Shams,

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  1. MBBS (Student), Intern, Department of medicine, Department of medicine, ESIC Hospital, Bihta, University of Messina, Patna, Italy, India

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Abstract

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nBackground: Cardiac troponins (cTn) represent the gold standard biomarkers for myocardial injury detection, yet their interpretation remains challenging due to various confounding factors and clinical contexts. Artificial intelligence (AI) technologies provide remarkable possibilities to improve the interpretation of troponin levels by utilizing pattern recognition, predictive modeling, and clinical decision-making support. Objective: This review examines the current state and future potential of AI-enhanced cardiac troponin interpretation, focusing on machine learning applications, predictive algorithms, and clinical implementation strategies. Methods: Comprehensive literature review of peer-reviewed articles, clinical studies, and technological developments in AI-assisted cardiac biomarker interpretation published between 2018-2024. Results: AI applications in troponin interpretation demonstrate significant promise in improving diagnostic accuracy, reducing false positives, predicting outcomes, and optimizing clinical workflows. Machine learning models demonstrate excellent accuracy in differentiating acute coronary syndromes from other reasons for elevated troponin levels. Conclusions: AI-enhanced troponin interpretation represents a paradigm shift toward precision cardiology, offering improved diagnostic accuracy and personalized risk stratification. However, successful implementation requires addressing challenges related to algorithm validation, regulatory approval, and clinical integration.nn

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Keywords: Cardiac troponins, Artificial intelligence, Machine learning, Myocardial injury, Precision medicine, Clinical decision support

n[if 424 equals=”Regular Issue”][This article belongs to Research and Reviews: A Journal of Medicine ]

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How to cite this article:
nAdnan shams, Bushra Shams. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]AI-Enhanced Interpretation of Cardiac Troponins: Toward Predictive Precision in Myocardial Injury[/if 2584]. Research and Reviews: A Journal of Medicine. 07/10/2025; 15(03):-.

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How to cite this URL:
nAdnan shams, Bushra Shams. [if 2584 equals=”][226 striphtml=1][else]AI-Enhanced Interpretation of Cardiac Troponins: Toward Predictive Precision in Myocardial Injury[/if 2584]. Research and Reviews: A Journal of Medicine. 07/10/2025; 15(03):-. Available from: https://journals.stmjournals.com/rrjom/article=07/10/2025/view=0

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References n

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  1. Apple FS, Collinson PO. Analytical characteristics of high-sensitivity cardiac troponin assays. Clin Chem. 2012 Jan;58(1):54–61.
  2. Thygesen K, Alpert JS, Jaffe AS, Chaitman BR, Bax JJ, Morrow DA, et al. Fourth universal definition of myocardial infarction (2018). J Am Coll Cardiol. 2018 Oct 30;72(18):2231–64.
  3. Januzzi JL Jr, Sandoval Y. The many faces of high-sensitivity cardiac troponin. J Am Coll Cardiol. 2019 Jul 2;74(1):94–6.
  4. Reichlin T, Hochholzer W, Bassetti S, Steuer S, Stelzig C, Hartwiger S, et al. Early diagnosis of myocardial infarction with sensitive cardiac troponin assays. N Engl J Med. 2009 Aug 27;361(9):858–67.
  5. Body R, Burrows G, Carley S, Lewis PS, Browning M, Mahler SA, et al. High-sensitivity cardiac troponin T concentrations below the limit of detection to exclude acute myocardial infarction: a prospective observational study. Clin Chem. 2015 Jul;61(7):983–9.
  6. Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T. Artificial intelligence in precision cardiovascular medicine. J Am Coll Cardiol. 2017 May 30;69(21):2657–64.
  7. Johnson KW, Torres Soto J, Glicksberg BS, Shameer K, Miotto R, Ali M, et al. Artificial intelligence in cardiology. J Am Coll Cardiol. 2018 Jun 12;71(23):2668–79.
  8. Dey D, Slomka PJ, Leeson P, Comaniciu D, Shrestha S, Arsanjani R, et al. Artificial intelligence in cardiovascular imaging: JACC state-of-the-art review. J Am Coll Cardiol. 2019 Mar 19;73(11):1317–35.
  9. Gordon AM, Homsher E, Regnier M. Regulation of contraction in striated muscle. Physiol Rev. 2000 Apr;80(2):853–924.
  10. Cummins B, Auckland ML, Cummins P. Cardiac-specific troponin-I radioimmunoassay in the diagnosis of acute myocardial infarction. Am Heart J. 1987 Jun;113(6):1333–44.
  11. Katus HA, Remppis A, Looser S, Hallermeier K, Scheffold T, Kübler W. Enzyme linked immuno assay of cardiac troponin T for the detection of acute myocardial infarction in patients. J Mol Cell Cardiol. 1989 Dec;21(12):1349–53.
  12. Apple FS, Jesse RL, Newby LK, Wu AH, Christenson RH. National Academy of Clinical Biochemistry and IFCC Committee for Standardization of Markers of Cardiac Damage Laboratory Medicine Practice Guidelines. Clin Chem. 2007 Apr;53(4):547–51.
  13. Roffi M, Patrono C, Collet JP, Mueller C, Valgimigli M, Andreotti F, et al. 2015 ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation. Eur Heart J. 2016 Jan 14;37(3):267–315.
  14. Neumann JT, Sörensen NA, Schwemer T, Ojeda F, Bourry R, Sciacca V, et al. Diagnosis of myocardial infarction using a high-sensitivity troponin I 1-hour algorithm. JAMA Cardiol. 2016 Jul 1;1(4):397–404.
  15. Sandoval Y, Smith SW, Thordsen SE, Apple FS. Supply/demand type 2 myocardial infarction: should we be paying more attention? J Am Coll Cardiol. 2014 May 27;63(20):2079–87.
  16. Kelley WE, Januzzi JL, Christenson RH. Increases of cardiac troponin in conditions other than acute coronary syndrome and heart failure. Clin Chem. 2009 Dec;55(12):2098–112.
  17. Twerenbold R, Boeddinghaus J, Nestelberger T, Wildi K, Puelacher C, Rubini Gimenez M, et al. Clinical use of high-sensitivity cardiac troponin in patients with suspected myocardial infarction. J Am Coll Cardiol. 2017 Aug 22;70(8):996–1012.
  18. Amsterdam EA, Wenger NK, Brindis RG, Casey DE Jr, Ganiats TG, Holmes DR Jr, et al. 2014 AHA/ACC Guideline for the management of patients with non-ST-elevation acute coronary syndromes. Circulation. 2014 Dec 23;130(25):e344–426.
  19. Thygesen K, Alpert JS, Jaffe AS, Simoons ML, Chaitman BR, White HD, et al. Third universal definition of myocardial infarction. Circulation. 2012 Oct 16;126(16):2020–35.
  20. Mueller C, Giannitsis E, Christ M, Ordonez-Llanos J, deFilippi C, McCord J, et al. Multicenter evaluation of a 0-hour/1-hour algorithm in the diagnosis of myocardial infarction with high-sensitivity cardiac troponin T. Ann Emerg Med. 2016 Jul;68(1):76–87.
  21. Sandoval Y, Apple FS, Mahler SA, Body R, Collinson PO, Jaffe AS. High-sensitivity cardiac troponin and the 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR guideline for the evaluation and diagnosis of chest pain. Circulation. 2022;146(8):569-81. doi:10.1161/CIRCULATIONAHA.122.059633
  22. Mitchell TM. Machine learning. New York: McGraw-Hill; 1997.
  23. Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference, and prediction. 2nd ed. New York: Springer; 2009.
  24. James G, Witten D, Hastie T, Tibshirani R. An introduction to statistical learning: with applications in R. New York: Springer; 2013.
  25. Ng AY, Jordan MI. On discriminative vs. generative classifiers: A comparison of logistic regression and naive Bayes. Adv Neural Inf Process Syst. 2002;14:841-8.
  26. Libbrecht MW, Noble WS. Machine learning applications in genetics and genomics. Nat Rev Genet. 2015;16(6):321-32. doi:10.1038/nrg3920
  27. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-44. doi:10.1038/nature14539
  28. Goodfellow I, Bengio Y, Courville A. Deep learning. Cambridge: MIT Press; 2016.
  29. Hirschberg J, Manning CD. Advances in natural language processing. Science. 2015;349(6245):261-6. doi:10.1126/science.aaa8685
  30. Kreimeyer K, Foster M, Pandey A, Arya N, Halford G, Jones SF, et al. Natural language processing systems for capturing and standardizing unstructured clinical information: a systematic review. J Biomed Inform. 2017;73:14-29. doi:10.1016/j.jbi.2017.07.014
  31. Dietterich TG. Ensemble methods in machine learning. In: Multiple classifier systems. Berlin: Springer; 2000. p. 1-15.
  32. Zhou ZH. Ensemble methods: foundations and algorithms. Boca Raton: CRC Press; 2012.
  33. Al’Aref SJ, Anchouche K, Singh G, Slomka PJ, Kolli KK, Kumar A, et al. Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. Eur Heart J. 2019;40(24):1975-86. doi:10.1093/eurheartj/ehy404
  34. Commandeur F, Slomka PJ, Goeller M, Cadet S, Razipour A, Gransar H, et al. Machine learning to predict the long-term risk of myocardial infarction and cardiac death based on clinical risk, coronary calcium, and epicardial adipose tissue: a prospective study. Cardiovasc Res. 2020;116(14):2216-25. doi:10.1093/cvr/cvz256
  35. Sandoval Y, Smith SW, Apple FS. Present and future of cardiac troponin in clinical practice: a paradigm shift to high-sensitivity assays. Am J Med. 2016;129(4):354-65. doi:10.1016/j.amjmed.2015.11.024
  36. Sandoval Y, Jaffe AS. Type 2 myocardial infarction: JACC review topic of the week. J Am Coll Cardiol. 2019;73(14):1846-60. doi:10.1016/j.jacc.2019.01.041
  37. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347-58. doi:10.1056/NEJMra1814259
  38. Krumholz HM. Big data and new knowledge in medicine: the thinking, training, and tools needed for a learning health system. Health Aff. 2014;33(7):1163-70. doi:10.1377/hlthaff.2014.0053
  39. Mahler SA, Riley RF, Hiestand BC, Russell GB, Hoekstra JW, Lefebvre CW, et al. The HEART pathway randomized trial: identifying emergency department patients with acute chest pain for early discharge. Circ Cardiovasc Qual Outcomes. 2015;8(2):195-203. doi:10.1161/CIRCOUTCOMES.114.001384
  40. Poldervaart JM, Langedijk M, Backus BE, Dekker IM, Six AJ, Doevendans PA, et al. Comparison of the GRACE, HEART and TIMI score to predict major adverse cardiac events in chest pain patients at the emergency department. Int J Cardiol. 2017;227:656-61. doi:10.1016/j.ijcard.2016.10.080
  41. Body R, Carley S, McDowell G, Parris R, Richards J, Cruickshank K, et al. Rapid exclusion of acute myocardial infarction in patients with undetectable troponin using a high-sensitivity assay. J Am Coll Cardiol. 2011;58(13):1332–9.
  42. Shah AS, Anand A, Sandoval Y, Lee KK, Smith SW, Adamson PD, et al. High-sensitivity cardiac troponin I at presentation in patients with suspected acute coronary syndrome: a cohort study. Lancet. 2015;386(10012):2481–8.
  43. Harrison RF, Kennedy RL. Artificial neural network models for prediction of acute coronary syndromes using clinical data from the time of presentation. Ann Emerg Med. 2005;46(5):431–9.
  44. Baim DS, Knatterud GL, Goldberg RJ, Anbe DT, Grover FL, McMahon RP, et al. A clinical trial comparing primary angioplasty with tissue plasminogen activator for acute myocardial infarction. N Engl J Med. 1997;336(23):1621–8.
  45. Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med. 2015;372(9):793–5.
  46. Shah SJ, Katz DH, Selvaraj S, Burke MA, Yancy CW, Gheorghiade M, et al. Phenomapping for novel classification of heart failure with preserved ejection fraction. Circulation. 2015;131(3):269–79.
  47. Leopold JA, Loscalzo J. Emerging role of precision medicine in cardiovascular disease. Circ Res. 2018;122(9):1302–15.
  48. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56.
  49. Beam AL, Kohane IS. Big data and machine learning in health care. JAMA. 2018;319(13):1317–8.
  50. Chen JH, Asch SM. Machine learning and prediction in medicine–beyond the peak of inflated expectations. N Engl J Med. 2017;376(26):2507–9.
  51. Wiens J, Shenoy ES. Machine learning for healthcare: on the verge of a major shift in healthcare epidemiology. Clin Infect Dis. 2018;66(1):149–53.
  52. Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2(10):719–31.
  53. Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, et al. A guide to deep learning in healthcare. Nat Med. 2019;25(1):24–9.
  54. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2(4):230–43.
  55. Shortliffe EH, Sepúlveda MJ. Clinical decision support in the era of artificial intelligence. JAMA. 2018;320(21):2199–200.
  56. Sutton RT, Pincock D, Baumgart DC, Sadowski DC, Fedorak RN, Kroeker KI. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit Med. 2020;3:17.
  57. Muehlematter UJ, Daniore P, Vokinger KN. Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015–20): a comparative analysis. Lancet Digit Health. 2021;3(3):e195–203.
  58. Babic B, Gerke S, Evgeniou T, Cohen IG. Algorithms on regulatory lockdown in medicine. Science. 2019;366(6470):1202–4.
  59. Liu X, Faes L, Kale AU, Wagner SK, Fu DJ, Bruynseels A, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit Health. 2019;1(6):e271–97.
  60. Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17(1):195.

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Volume 15
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03
Received 01/07/2025
Accepted 01/08/2025
Published 07/10/2025
Retracted
Publication Time 98 Days

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