International Journal of Heart Failure

The Korean Society of Heart Failure (대한심부전학회)
권호 표지
DOI QR코드

DOI QR Code

Application and Potential of Artificial Intelligence in Heart Failure: Past, Present, and Future

  • Minjae Yoon (Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine) ;
  • Jin Joo Park (Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine) ;
  • Taeho Hur (Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine) ;
  • Cam-Hao Hua (Department of Computer Science and Engineering, Kyung Hee University) ;
  • Musarrat Hussain (Department of Computer Science and Engineering, Kyung Hee University) ;
  • Sungyoung Lee (Department of Computer Science and Engineering, Kyung Hee University) ;
  • Dong-Ju Choi (Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine)
  • Received : 2023.09.05
  • Accepted : 2023.11.26
  • Published : 2024.01.31
Download PDF
⟨ Previous Next ⟩

Abstract

The prevalence of heart failure (HF) is increasing, necessitating accurate diagnosis and tailored treatment. The accumulation of clinical information from patients with HF generates big data, which poses challenges for traditional analytical methods. To address this, big data approaches and artificial intelligence (AI) have been developed that can effectively predict future observations and outcomes, enabling precise diagnoses and personalized treatments of patients with HF. Machine learning (ML) is a subfield of AI that allows computers to analyze data, find patterns, and make predictions without explicit instructions. ML can be supervised, unsupervised, or semi-supervised. Deep learning is a branch of ML that uses artificial neural networks with multiple layers to find complex patterns. These AI technologies have shown significant potential in various aspects of HF research, including diagnosis, outcome prediction, classification of HF phenotypes, and optimization of treatment strategies. In addition, integrating multiple data sources, such as electrocardiography, electronic health records, and imaging data, can enhance the diagnostic accuracy of AI algorithms. Currently, wearable devices and remote monitoring aided by AI enable the earlier detection of HF and improved patient care. This review focuses on the rationale behind utilizing AI in HF and explores its various applications.

Keywords

Acknowledgement

This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI21C1074).

References

  1. Conrad N, Judge A, Tran J, et al. Temporal trends and patterns in heart failure incidence: a population-based study of 4 million individuals. Lancet 2018;391:572-80.
  2. Park JJ, Lee CJ, Park SJ, et al. Heart failure statistics in Korea, 2020: a report from the Korean Society of Heart Failure. Int J Heart Fail 2021;3:224-36.
  3. Heidenreich PA, Bozkurt B, Aguilar D, et al. 2022 AHA/ACC/HFSA guideline for the management of heart failure: a report of the American College of Cardiology/American Heart Association joint committee on clinical practice guidelines. J Am Coll Cardiol 2022;79:e263-421.
  4. McDonagh TA, Metra M, Adamo M, et al. Corrigendum to: 2021 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure: developed by the task force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC) with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur Heart J 2021;42:4901.
  5. Cho JY, Cho DH, Youn JC, et al. Korean Society of Heart Failure guidelines for the management of heart failure: definition and diagnosis. Int J Heart Fail 2023;5:51-65.
  6. Youn JC, Kim D, Cho JY, et al. Korean Society of Heart Failure guidelines for the management of heart failure: treatment. Int J Heart Fail 2023;5:66-81.
  7. Lanzer JD, Leuschner F, Kramann R, Levinson RT, Saez-Rodriguez J. Big data approaches in heart failure research. Curr Heart Fail Rep 2020;17:213-24.
  8. Docherty AB, Lone NI. Exploiting big data for critical care research. Curr Opin Crit Care 2015;21:467-72.
  9. Averbuch T, Sullivan K, Sauer A, et al. Applications of artificial intelligence and machine learning in heart failure. Eur Heart J Digit Health 2022;3:311-22.
  10. Fan J, Lv J. Sure independence screening for ultrahigh dimensional feature space. J R Stat Soc Series B Stat Methodol 2008;70:849-911.
  11. Fan J, Han F, Liu H. Challenges of big data analysis. Natl Sci Rev 2014;1:293-314.
  12. Meng C, Zeleznik OA, Thallinger GG, Kuster B, Gholami AM, Culhane AC. Dimension reduction techniques for the integrative analysis of multi-omics data. Brief Bioinform 2016;17:628-41.
  13. Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Campbell JP. Introduction to machine learning, neural networks, and deep learning. Transl Vis Sci Technol 2020;9:14.
  14. Park SM, Lee SY, Jung MH, et al. Korean Society of Heart Failure guidelines for the management of heart failure: management of the underlying etiologies and comorbidities of heart failure. Korean Circ J 2023;53:425-51.
  15. Fu Y, Eisen HJ. Genetics of dilated cardiomyopathy. Curr Cardiol Rep 2018;20:121.
  16. Rau CD, Lusis AJ, Wang Y. Genetics of common forms of heart failure: challenges and potential solutions. Curr Opin Cardiol 2015;30:222-7.
  17. Hassani H, Silva ES, Unger S, TajMazinani M, Mac Feely S. Artificial intelligence (AI) or intelligence augmentation (IA): what is the future? AI 2020;1:143-55.
  18. Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol 2019;28:73-81.
  19. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med 2019;380:1347-58.
  20. Rajula HS, Verlato G, Manchia M, Antonucci N, Fanos V. Comparison of conventional statistical methods with machine learning in medicine: diagnosis, drug development, and treatment. Medicina (Kaunas) 2020;56:455.
  21. Weller DL, Love TM, Wiedmann M. Interpretability versus accuracy: a comparison of machine learning models built using different algorithms, performance measures, and features to predict E. coli levels in agricultural water. Front Artif Intell 2021;4:628441.
  22. Latif J, Xiao C, Imran A, Tu S. Medical imaging using machine learning and deep learning algorithms: a review. In: Proceedings of 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET); 2019 January 30-31; Sukkur, Pakistan. New York: IEEE; 2019. p.1-5.
  23. Johnson KW, Torres Soto J, Glicksberg BS, et al. Artificial intelligence in cardiology. J Am Coll Cardiol 2018;71:2668-79.
  24. Noorbakhsh-Sabet N, Zand R, Zhang Y, Abedi V. Artificial intelligence transforms the future of health care. Am J Med 2019;132:795-801.
  25. McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. 1943. Bull Math Biol 1990;52:99-115.
  26. Lee EJ, Kim YH, Kim N, Kang DW. Deep into the brain: artificial intelligence in stroke imaging. J Stroke 2017;19:277-85.
  27. Choi E, Schuetz A, Stewart WF, Sun J. Using recurrent neural network models for early detection of heart failure onset. J Am Med Inform Assoc 2017;24:361-70.
  28. Rao S, Li Y, Ramakrishnan R, et al. An explainable transformer-based deep learning model for the prediction of incident heart failure. IEEE J Biomed Health Inform 2022;26:3362-72.
  29. Gozalo-Brizuela R, Garrido-Merchan EC. ChatGPT is not all you need. A state of the art review of large Generative AI models. arXiv. 2023 January 11. Available from: https://doi.org/10.48550/arXiv.2301.04655.
  30. Kebaili A, Lapuyade-Lahorgue J, Ruan S. Deep learning approaches for data augmentation in medical imaging: a review. J Imaging 2023;9:81.
  31. Seah JC, Tang JS, Kitchen A, Gaillard F, Dixon AF. Chest radiographs in congestive heart failure: visualizing neural network learning. Radiology 2019;290:514-22.
  32. Harvey D, Lobban F, Rayson P, Warner A, Jones S. Natural language processing methods and bipolar disorder: scoping review. JMIR Ment Health 2022;9:e35928.
  33. Guo A, Pasque M, Loh F, Mann DL, Payne PR. Heart failure diagnosis, readmission, and mortality prediction using machine learning and artificial intelligence models. Curr Epidemiol Rep 2020;7:212-9.
  34. Choi E, Schuetz A, Stewart WF, Sun J. Medical concept representation learning from electronic health records and its application on heart failure prediction. arXiv. 2017 June 20. Available from: https://doi.org/10.48550/arXiv.1602.03686.
  35. Choi DJ, Park JJ, Ali T, Lee S. Artificial intelligence for the diagnosis of heart failure. NPJ Digit Med 2020;3:54.
  36. Nainwal A, Kumar Y, Jha B. Morphological changes in congestive heart failure ECG. In: Proceedings of 2016 2nd International Conference on Advances in Computing, Communication, & Automation (ICACCA) (Fall); 2016 September 30-October 1; Bareilly, India. New York: IEEE; 2016. p.1-4.
  37. Hendry PB, Krisdinarti L, Erika M. Scoring system based on electrocardiogram features to predict the type of heart failure in patients with chronic heart failure. Cardiol Rev 2016;7:110-6.
  38. 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.
  39. Kwon JM, Kim KH, Eisen HJ, et al. Artificial intelligence assessment for early detection of heart failure with preserved ejection fraction based on electrocardiographic features. Eur Heart J Digit Health 2020;2:106-16.
  40. Choi J, Lee S, Chang M, Lee Y, Oh GC, Lee HY. Deep learning of ECG waveforms for diagnosis of heart failure with a reduced left ventricular ejection fraction. Sci Rep 2022;12:1-10.
  41. Kwon JM, Kim KH, Jeon KH, et al. Development and validation of deep-learning algorithm for electrocardiography-based heart failure identification. Korean Circ J 2019;49:629-39.
  42. Unterhuber M, Rommel KP, Kresoja KP, et al. Deep learning detects heart failure with preserved ejection fraction using a baseline electrocardiogram. Eur Heart J Digit Health 2021;2:699-703.
  43. Bui AL, Horwich TB, Fonarow GC. Epidemiology and risk profile of heart failure. Nat Rev Cardiol 2011;8:30-41.
  44. Lee SE, Lee HY, Cho HJ, et al. Clinical characteristics and outcome of acute heart failure in Korea: results from the Korean Acute Heart Failure Registry (KorAHF). Korean Circ J 2017;47:341-53.
  45. Golas SB, Shibahara T, Agboola S, et al. A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data. BMC Med Inform Decis Mak 2018;18:44.
  46. Kwon JM, Kim KH, Jeon KH, et al. Artificial intelligence algorithm for predicting mortality of patients with acute heart failure. PLoS One 2019;14:e0219302.
  47. Boehmer JP, Hariharan R, Devecchi FG, et al. A multisensor algorithm predicts heart failure events in patients with implanted devices: results from the MultiSENSE study. JACC Heart Fail 2017;5:216-25.
  48. Shah SJ, Katz DH, Selvaraj S, et al. Phenomapping for novel classification of heart failure with preserved ejection fraction. Circulation 2015;131:269-79.
  49. Gevaert AB, Tibebu S, Mamas MA, et al. Clinical phenogroups are more effective than left ventricular ejection fraction categories in stratifying heart failure outcomes. ESC Heart Fail 2021;8:2741-54.
  50. Ahmad T, Lund LH, Rao P, et al. Machine learning methods improve prognostication, identify clinically distinct phenotypes, and detect heterogeneity in response to therapy in a large cohort of heart failure patients. J Am Heart Assoc 2018;7:e008081.
  51. Bazoukis G, Stavrakis S, Zhou J, et al. Machine learning versus conventional clinical methods in guiding management of heart failure patients-a systematic review. Heart Fail Rev 2021;26:23-34.
  52. Jing L, Ulloa Cerna AE, Good CW, et al. A machine learning approach to management of heart failure populations. JACC Heart Fail 2020;8:578-87.
  53. Sullivan K, Mamas MA, Van Spall HG. Machine learning could facilitate optimal titration of guideline-directed medical therapy in heart failure. J Am Coll Cardiol 2019;74:1424-5.
  54. Daubert C, Behar N, Martins RP, Mabo P, Leclercq C. Avoiding nonresponders to cardiac resynchronization therapy: a practical guide. Eur Heart J 2017;38:1463-72.
  55. Cikes M, Sanchez-Martinez S, Claggett B, et al. Machine learningbased phenogrouping in heart failure to identify responders to cardiac resynchronization therapy. Eur J Heart Fail 2019;21:74-85.
  56. Deng Y, Cheng S, Huang H, et al. Machine learning-based phenomapping in patients with heart failure and secondary prevention implantable cardioverter-defibrillator implantation: a proof-of-concept study. Rev Cardiovasc Med 2023;24:37.
  57. Shakibfar S, Krause O, Lund-Andersen C, et al. Predicting electrical storms by remote monitoring of implantable cardioverter-defibrillator patients using machine learning. Europace 2019;21:268-74.
  58. ElRefai M, Abouelasaad M, Wiles BM, et al. Role of deep learning methods in screening for subcutaneous implantable cardioverter defibrillator in heart failure. Ann Noninvasive Electrocardiol 2023;28:e13028.
  59. Dunn AJ, ElRefai MH, Roberts PR, Coniglio S, Wiles BM, Zemkoho AB. Deep learning methods for screening patients' S-ICD implantation eligibility. Artif Intell Med 2021;119:102139.
  60. Yasmin F, Shah SM, Naeem A, et al. Artificial intelligence in the diagnosis and detection of heart failure: the past, present, and future. Rev Cardiovasc Med 2021;22:1095-113.
  61. Cho J, Lee B, Kwon JM, et al. Artificial intelligence algorithm for screening heart failure with reduced ejection fraction using electrocardiography. ASAIO J 2021;67:314-21.
  62. Bhatia A, Maddox TM. Remote patient monitoring in heart failure: factors for clinical efficacy. Int J Heart Fail 2020;3:31-50.
  63. Kwon JM, Jo YY, Lee SY, et al. Artificial intelligence-enhanced smartwatch ECG for heart failure-reduced ejection fraction detection by generating 12-lead ECG. Diagnostics (Basel) 2022;12:654.
  64. Stehlik J, Schmalfuss C, Bozkurt B, et al. Continuous wearable monitoring analytics predict heart failure hospitalization: the LINKHF multicenter study. Circ Heart Fail 2020;13:e006513.
  65. Breck E, Polyzotis N, Roy S, Whang S, Zinkevich M. Data validation for machine learning. In: Proceedings of the 2nd SysML Conference; Palo Alto, CA, USA; 2019.
  66. Emmanuel T, Maupong T, Mpoeleng D, Semong T, Mphago B, Tabona O. A survey on missing data in machine learning. J Big Data 2021;8:140.
  67. Su J, Vargas DV, Sakurai K. One pixel attack for fooling deep neural networks. IEEE Trans Evol Comput 2019;23:828-41.
  68. Dombrowski AK, Alber M, Anders C, Ackermann M, Muller KR, Kessel P. Explanations can be manipulated and geometry is to blame. In: 33rd Conference on Neural Information Processing Systems (NeurIPS 2019); Vancouver, Canada; 2019.
  69. Ghorbani A, Abid A, Zou J. Interpretation of neural networks is fragile. Proc Conf AAAI Artif Intell 2019;33:3681-8.
  70. Pal A, Umapathi LK, Sankarasubbu M. Med-HALT: medical domain hallucination test for large language models. arXiv. 2023 October 14. Available from: https://doi.org/10.48550/arXiv.2307.15343.
  71. Nori H, King N, McKinney SM, Carignan D, Horvitz E. Capabilities of GPT-4 on medical challenge problems. arXiv. 2023 April 12. Available from: https://doi.org/10.48550/arXiv.2303.13375.