Yapay Zekanın Kardiyovasküler Hastalıklar Alanında Uygulanması Hem Teşhis Hem de Tedavi Yönlerine Odaklanmaktadır.

Yıl 2024, Cilt: 5 Sayı: 2, 22 - 35, 14.06.2024

Lütfü Aşkın , Esra Polat , Yusuf Hoşoğlu , Okan Tanrıverdi

https://doi.org/10.46871/eams.1438927

Öz

Yapay zeka (YZ), tıp alanında gelişmiş bilgisayar algoritmaları kullanarak büyük veri tabanlarından bilgi almak için kullanılır. YZ, kalp yetmezliği, atriyal fibrilasyon, kalp kapak hastalığı, hipertrofik kardiyomiyopati, konjenital kalp hastalığı ve diğerleri gibi durumlar da dahil olmak üzere kardiyovasküler hastalıkların (KVH'ler) tanımlanmasını ve yönetimini hızlandırma potansiyeline sahiptir. Klinik bir perspektiften bakıldığında, YZ KVH tanısını geliştirir, yardımcı araçların kullanışlılığını artırır, farklı hastalık türlerinin sınıflandırılmasına ve tanımlanmasına yardımcı olur ve sonuçların doğru tahmin edilmesini sağlar. Kapsamlı sağlık verilerinden küçük bağlantılar çıkarmak için tasarlanan son teknoloji YZ algoritmalarının, önceki yöntemlere kıyasla daha zorlu görevleri ele alması beklenmektedir. Bu araştırmanın amacı, YZ'nin KVH'lerdeki mevcut kullanımlarını vurgulamak ve böylece bilgisayar bilimleri konusunda çok az bilgisi olan doktorları daha derin bir anlayış kazanmaları ve klinik uygulamada YZ algoritmalarını etkili bir şekilde kullanmaları için donatmaktır.

Anahtar Kelimeler

ileri bilgisayar algoritmaları, yapay zeka, kardiyovasküler hastalıklar

The Application of Artificial Intelligence in the Field of Cardiovascular Diseases Focuses on Both Diagnostic and Therapeutic Aspects.

Öz

Artificial intelligence (AI) is used in the field of medicine to retrieve information from large databases by using advanced computer algorithms. AI has the potential to accelerate the identification and management of cardiovascular diseases (CVDs), including conditions such as heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease, and several others. From a clinical perspective, AI enhances the diagnosis of CVD, increases the usefulness of auxiliary tools, aids in stratifying and identifying different types of diseases, and enables accurate prediction of outcomes. State-of-the-art AI algorithms, designed to extract minute connections from extensive healthcare data, are anticipated to address more challenging tasks compared to earlier methods. The goal of this research is to emphasize the current uses of AI in CVDs, thereby equipping doctors with little knowledge in computer science to gain a deeper understanding and effectively use AI algorithms in clinical practice.

Anahtar Kelimeler

advanced computer algoritms, artificial intelligence, cardiovascular diseases

Kaynakça

• 1. Xu D, Liu R, Xu H, et al. Adoption of twodimensional ultrasound gastrointestinal flling contrast on artifcial intelligence algorithm in clinical diagnosis of gastric cancer. Comput Math Methods Med 2022;2022:7385344.
• 2. Montull L, Slapsinskaite-Dackeviciene A, Kiely J, et al. Integrative proposals of sports monitoring: subjective outperforms objective monitoring. Sports Med Open 2022;8:41.
• 3. Attia ZI, Kapa S, Lopez-Jimenez F, et al. Screening for cardiac contractile dysfunction using an artifcial intelligence-enabled electrocardiogram. Nat Med 2019;25:70–4.
• 4. Attia ZI, Kapa S, Noseworthy PA, et al. Artifcial intelligence ECG to detect left ventricular dysfunction in COVID-19:a case series. Mayo Clin Proc 2020;95:2464–6.
• 5. Yao X, Rushlow DR, Inselman JW, et al. Artifcial intelligence-enabled electrocardiograms for identifcation of patients with low ejection fraction: a pragmatic, randomized clinical trial. Nat Med 2021;27:815–9.
• 6. Attia ZI, Noseworthy PA, Lopez-Jimenez F, et al. An artifcial intelligence-enabled ECG algorithm for the identifcation of patients with atrial fbrillation during sinüs rhythm: a retrospective analysis of outcome prediction. Lancet 2019;394:861–7.
• 7. Kwon JM, Cho Y, Jeon KH, et al. A deep learning algorithm to detect anaemia with ECGs: a retrospective, multicentre study. Lancet Digit Health 2020;2:e358–67.
• 8. Ko WY, Siontis KC, Attia ZI, et al. Detection of hypertrophic cardiomyopathy using a convolutional neural network-enabled electrocardiogram. J Am Coll Cardiol 2020;75:722–33.
• 9. Kwon JM, Kim KH, Medina-Inojosa J, et al. Artifcial intelligence for early prediction of pulmonary hypertension using electrocardiography. J Heart Lung Transplant 2020;39:805–14.
• 10. Cho H, Keenan G, Madandola OO, et al. Assessing the usability of a clinical decision support system: heuristic evaluation. JMIR Hum Factors 2022;9:e31758.
• 11. Emile SH, Hamid HKS. Fighting COVID-19, a place for artifcial intelligence. Transbound Emerg Dis 2020;67:1754–5
• 12. Zhu R, Jiang C, Wang X, et al. Privacy-preserving construction of generalized linear mixed model for biomedical computation. Bioinformatics 2020;36:128–35.
• 13. Yadav RS. Data analysis of COVID-2019 epidemic using machine learning methods: a case study of India. Int J Inf Technol 2020;12:1321–30.
• 14. Kahr M, Kovacs G, Loinig M, et al. Condition monitoring of ball bearings based on machine learning with synthetically generated data. Sensors 2022;22:7.
• 15. Muller E, Arnold E, Breitwieser O, et al. A scalable approach to modeling on accelerated neuromorphic hardware. Front Neurosci 2022;16:884128.
• 16. Yadav AK, Banerjee SK, Das B, et al. Editorial: systems biology and omics approaches for understanding complex disease biology. Front Genet 2022;13:896818.
• 17. de Mattos Neto PSG, de Oliveira JFL, et al. Energy consumption forecasting for smart meters using extreme learning machine ensemble. Sensors 2021;21:23.
• 18. Krittanawong C, Zhang H, Wang Z, et al. Artifcial intelligence in precision cardiovascular medicine. J Am Coll Cardiol 2017;69:2657–64.
• 19. Lenstrup M, Kjaergaard J, Petersen CL, et al. Evaluation of left ventricular mass measured by 3D echocardiography using magnetic resonance imaging as gold standard. Scand J Clin Lab Invest 2006;66:647–57.
• 20. Vaid A, Johnson KW, Badgeley MA, et al. Using deep-learning algorithms to simultaneously identify right and left ventricular dysfunction from the electrocardiogram. JACC Cardiovasc Imaging. 2022;15:395–410.
• 21. Saikrishnan N, Kumar G, Sawaya FJ, et al. Accurate assessment of aortic stenosis: a review of diagnostic modalities and hemodynamics. Circulation 2014;129:244–53.
• 22. Japp AG, Gulati A, Cook SA, et al. The diagnosis and evaluation of dilated cardiomyopathy. J Am Coll Cardiol 2016;67:2996–3010.
• 23. Shrivastava S, Cohen-Shelly M, Attia ZI, et al. Artifcial intelligence-enabled electrocardiography to screen patients with dilated cardiomyopathy. Am J Cardiol 2021;155:121–7.
• 24. Elias P, Poterucha TJ, Rajaram V, et al. Deep learning electrocardiographic analysis for detection of left-sided valvular heart disease. J Am Coll Cardiol 2022;80:613–26.
• 25. Siontis KC, Noseworthy PA, Attia ZI, et al. Artifcial intelligenceenhanced electrocardiography in cardiovascular disease management. Nat Rev Cardiol 2021;18:465–78.
• 26. Attia ZI, Harmon DM, Behr ER, et al. Application of artifcial intelligence to the electrocardiogram. Eur Heart J 2021;42:4717–30.
• 27. Lancellotti P, Magne J, Dulgheru R, et al. Outcomes of patients with asymptomatic aortic stenosis followed up in heart valve clinics. JAMA Cardiol 2018;3:1060–8.
• 28. Leon MB, Smith CR, Mack M, et al. Transcatheter aortic-valve implantation for aortic stenosis in patients who cannot undergo surgery. N Engl J Med 2010;363:1597–607.
• 29. Kwon JM, Lee SY, Jeon KH, et al. Deep learningbased algorithm for detecting aortic stenosis using electrocardiography. J Am Heart Assoc 2020;9:e014717.
• 30. Cohen-Shelly M, Attia ZI, Friedman PA, et al. Electrocardiogram screening for aortic valve stenosis using artifcial intelligence. Eur Heart J 2021;42:2885–96.
• 31. Siontis KC, Gersh BJ, Killian JM, et al. Typical, atypical, and asymptomatic presentations of new-onset atrial fbrillation in the community: characteristics and prognostic implications. Heart Rhythm 2016;13:1418–24.
• 32. Davidson KW, Barry MJ, Mangione CM, et al. Screening for atrial fibrillation: US preventive services task force recommendation statement. JAMA 2022;327:360–7.
• 33. Khurshid S, Friedman S, Reeder C, et al. ECG-based deep learning and clinical risk factors to predict atrial fibrillation. Circulation 2022;145:122–33.
• 34. Noseworthy PA, Attia ZI, Behnken EM, et al. Artifcial intelligence-guided screening for atrial fibrillation using electrocardiogram during sinus rhythm: a prospective non-randomised interventional trial. Lancet 2022;400:1206–12.
• 35. Sun X, Yin Y, Yang Q, et al. Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives. Eur J Med Res 2023;28:242.
• 36. Betancur J, Commandeur F, Motlagh M, et al. Deep learning for prediction of obstructive disease from fast myocardial perfusion SPECT: a multicenter study. JACC Cardiovasc Imaging 2018;11:1654–63.
• 37. Christofersen M, Tybjærg-Hansen A. Visible aging signs as risk markers for ischemic heart disease: epidemiology, pathogenesis and clinical implications. Ageing Res Rev 2016;25:24–41.
• 38. Lin S, Li Z, Fu B, et al. Feasibility of using deep learning to detect coronary artery disease based on facial photo. Eur Heart J 2020;41:4400–11.
• 39. Yan BP, Lai WHS, Chan CKY, et al. Highthroughput, contact-free detection of atrial fibrillation from video with deep learning. JAMA Cardiol 2020;5:105–7.
• 40. de Couto G, Ouzounian M, Liu PP. Early detection of myocardial dysfunction and heart failure. Nat Rev Cardiol 2010;7:334–44.
• 41. Khurshid S, Friedman S, Pirruccello JP, Di Achille P, Diamant N, Anderson CD, et al. Deep learning to predict cardiac magnetic resonance-derived left ventricular mass and hypertrophy from 12-lead ECGs. Circ Cardiovasc Imaging 2021;14:e012281.
• 42. Liu CM, Chang SL, Chen HH, et al. The clinical application of the deep learning technique for predicting trigger origins in patients with paroxysmal atrial fbrillation with catheter ablation. Circ Arrhythm Electrophysiol 2020;13:e008518.
• 43. Arnaout R, Curran L, Zhao Y, et al. An ensemble of neural networks provides expert-level prenatal detection of complex congenital heart disease. Nat Med 2021;27:882–91.
• 44. Donofrio MT, Moon-Grady AJ, Hornberger LK, et al. Diagnosis and treatment of fetal cardiac disease: a scientifc statement from the American heart association. Circulation 2014;129:2183–242.
• 45. Sun HY, Proudfoot JA, McCandless RT. Prenatal detection of critical cardiac outfow tract anomalies remains suboptimal despite revised obstetrical imaging guidelines. Congenit Heart Dis 2018;13:748–56.
• 46. Cikes M, Sanchez-Martinez S, Claggett B, et al. Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy. Eur J Heart Fail 2019;21:74–85.
• 47. Karwath A, Bunting KV, Gill SK, et al. Redefning beta-blocker response in heart failure patients with sinus rhythm and atrial fbrillation: a machine learning cluster analysis. Lancet 2021;398:1427–35.
• 48. Boriani G, Vitolo M, Diemberger I, et al. Optimizing indices of atrial fbrillation susceptibility and burden to evaluate atrial fbrillation severity, risk and outcomes. Cardiovasc Res 2021;117:1–21.
• 49. Proietti M, Vitolo M, Harrison SL, et al. Impact of clinical phenotypes on management and outcomes in European atrial fbrillation patients: a report from the ESC-EHRA EURObservational research programme in AF (EORP-AF) general long-term registry. BMC Med 2021;19:256.
• 50. Howard JP, Cook CM, van de Hoef TP, et al. Artifcial intelligence for aortic pressure waveform analysis during coronary angiography: machine learning for patient safety. JACC Cardiovasc Interv 2019;12:2093–101.
• 51. Yang DY, Nie ZQ, Liao LZ, et al. Phenomapping of subgroups in hypertensive patients using unsupervised datadriven cluster analysis: an exploratory study of the SPRINT trial. Eur J Prev Cardiol 2019;26:1693–706.
• 52. Reel PS, Reel S, van Kralingen JC, et al. Machine learning for classifcation of hypertension subtypes using multiomics: a multi-centre, retrospective, data-driven study. EBioMedicine 2022;84:104276.
• 53. Zhou H, Li L, Liu Z, et al. Deep learning algorithm to improve hypertrophic cardiomyopathy mutation prediction using cardiac cine images. Eur Radiol 2021;31:3931–40.
• 54. Raghunath S, Ulloa Cerna AE, Jing L, et al. Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network. Nat Med 2020;26:886–91.
• 55. Toya T, Ahmad A, Attia Z, et al. Vascular aging detected by peripheral endothelial dysfunction is associated with ECG-derived physiological aging. J Am Heart Assoc 2021;10:e018656.
• 56. Cheung CY, Xu D, Cheng CY, et al. A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre. Nat Biomed Eng 2021;5:498–508.
• 57. de Souza ESCG, Buginga GC, de Souza ESEA, et al. Prediction of mortality in coronary artery disease: role of machine learning and maximal exercise capacity. Mayo Clin Proc 2022;97:1472–82.
• 58. Backhaus SJ, Aldehayat H, Kowallick JT, et al. Artifcial intelligence fully automated myocardial strain quantifcation for risk stratifcation following acute myocardial infarction. Sci Rep 2022;12:12220.
• 59. Zeleznik R, Foldyna B, Eslami P, et al. Deep convolutional neural networks to predict cardiovascular risk from computed tomography. Nat Commun 2021;12:715.
• 60. Min HS, Ryu D, Kang SJ, et al. Prediction of coronary stent underexpansion by pre-procedural intravascular ultrasound based deep learning. JACC Cardiovasc Interv 2021;14:1021–9.
• 61. Goto S, Goto S, Pieper KS, et al. New artifcial intelligence prediction model using serial prothrombin time international normalized ratio measurements in atrial fbrillation patients on vitamin K antagonists: GARFIELD-AF. Eur Heart J Cardiovasc Pharmacother 2020;6:301–9.
• 62. Kilic A, Goyal A, Miller JK, et al. Performance of a machine learning algorithm in predicting outcomes of aortic valve replacement. Ann Thorac Surg 2021;111:503–10.
• 63. Sherman E, Alejo D, Wood-Doughty Z, et al. Leveraging machine learning to predict 30-day hospital readmission after cardiac surgery. Ann Thorac Surg 2022;114:2173–9.