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image of Artificial Intelligence for Cardiovascular Diseases

Abstract

Globally, cardiovascular disease [CVD] continues to be a major cause of death. Advancements in Artificial Intelligence [AI] in recent times present revolutionary opportunities for the diagnosis, treatment, and prevention of this condition. In this paper, we review mainly the applications of AI in CVDs with its limitations and challenges. Artificial intelligence [AI] algorithms can quickly and precisely analyze medical images, such as CT scans, X-rays, and ECGs, helping with early and more accurate identification of a variety of CVD diseases. To identify those who are at a high risk of getting CVD, AI models can also analyze patient data. This allows for early intervention and preventive measures. AI systems are also capable of analyzing complicated medical data to provide individualized therapy recommendations based on the requirements and traits of each patient. During patient meetings, AI-powered solutions can also help healthcare practitioners by offering real-time insights and recommendations, which may improve treatment outcomes. Machine learning [ML], which is a branch of AI and computer sciences, has also been employed to uncover complex interactions among clinical variables, leading to more accurate predictive models for major adverse cardiovascular events [MACE] like combining clinical data with stress test results has improved the detection of myocardial ischemia, enhancing the ability to predict future cardiovascular outcomes. In this paper, we will focus on the current AI applications in different CVDs. Also, precision medicine, and targeted therapy for these cardiovascular problems will be discussed.

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/content/journals/rascs/10.2174/0126662558348090241210063629
2024-12-30
2025-03-01
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References

  1. Goldsborough E. III Osuji N. Blaha M.J. Assessment of Cardiovascular Disease Risk. Endocrinol. Metab. Clin. North Am. 2022 51 3 483 509 10.1016/j.ecl.2022.02.005 35963625
    [Google Scholar]
  2. Yasmin F. Shah S.M.I. Naeem A. Shujauddin S.M. Jabeen A. Kazmi S. Siddiqui S.A. Kumar P. Salman S. Hassan S.A. Dasari C. Choudhry A.S. Mustafa A. Chawla S. Lak H.M. Artificial intelligence in the diagnosis and detection of heart failure: the past, present, and future. Rev. Cardiovasc. Med. 2021 22 4 1095 1113 10.31083/j.rcm2204121 34957756
    [Google Scholar]
  3. Chen M. Decary M. Artificial intelligence in healthcare: An essential guide for health leaders. Healthc. Manage. Forum 2020 33 1 10 18 10.1177/0840470419873123 31550922
    [Google Scholar]
  4. Pastorino R. De Vito C. Migliara G. Glocker K. Binenbaum I. Ricciardi W. Boccia S. Benefits and challenges of Big Data in healthcare: an overview of the European initiatives. Eur. J. Public Health 2019 29 Suppl. 3 23 27 10.1093/eurpub/ckz168 31738444
    [Google Scholar]
  5. Dilsizian S.E. Siegel E.L. Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment. Curr. Cardiol. Rep. 2014 16 1 441 10.1007/s11886‑013‑0441‑8 24338557
    [Google Scholar]
  6. Kulkarni P. Mahadevappa M. Chilakamarri S. The Emergence of Artificial Intelligence in Cardiology: Current and Future Applications. Curr. Cardiol. Rev. 2022 18 3 e191121198124 10.2174/1573403X17666211119102220 34802407
    [Google Scholar]
  7. Mathur P. Srivastava S. Xu X. Mehta J.L. Artificial Intelligence, Machine Learning, and Cardiovascular Disease. Clin. Med. Insights Cardiol. 2020 14 10.1177/1179546820927404 32952403
    [Google Scholar]
  8. Yan Y. Zhang J.W. Zang G.Y. Pu J. The primary use of artificial intelligence in cardiovascular diseases: what kind of potential role does artificial intelligence play in future medicine? J. Geriatr. Cardiol. 2019 16 8 585 591 31555325
    [Google Scholar]
  9. Sun X. Yin Y. Yang Q. Huo T. Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives. Eur. J. Med. Res. 2023 28 1 242 10.1186/s40001‑023‑01065‑y 37475050
    [Google Scholar]
  10. Ali A. Hu B. Ramahi O. Intelligent detection of cracks in metallic surfaces using a waveguide sensor loaded with metamaterial elements. Sensors 2015 15 5 11402 11416 10.3390/s150511402 25988871
    [Google Scholar]
  11. Yadav A.K. Banerjee S.K. Das B. Chaudhary K. Editorial: Systems Biology and Omics Approaches for Understanding Complex Disease Biology. Front. Genet. 2022 13 896818 10.3389/fgene.2022.896818 35495146
    [Google Scholar]
  12. Krittanawong C. Zhang H. Wang Z. Aydar M. Kitai T. Artificial Intelligence in Precision Cardiovascular Medicine. J. Am. Coll. Cardiol. 2017 69 21 2657 2664 10.1016/j.jacc.2017.03.571 28545640
    [Google Scholar]
  13. Mintz Y Brodie R Introduction to artificial intelligence in medicine Minim. Invasive Ther. Allied Technol. 2019 28 2 73 81 10.1080/13645706.2019.1575882
    [Google Scholar]
  14. Stuckey T. Meine F. McMinn T. Depta J.P. Bennett B. McGarry T. Carroll W. Suh D. Steuter J.A. Roberts M. Gillins H.R. Lange E. Fathieh F. Burton T. Khosousi A. Shadforth I. Sanders W.E. Jr Rabbat M.G. Development and validation of a machine learned algorithm to IDENTIFY functionally significant coronary artery disease. Front. Cardiovasc. Med. 2022 9 956147 10.3389/fcvm.2022.956147 36119746
    [Google Scholar]
  15. Lin A. Kolossváry M. Motwani M. Išgum I. Maurovich-Horvat P. Slomka P.J. Dey D. Artificial Intelligence in Cardiovascular Imaging for Risk Stratification in Coronary Artery Disease. Radiol. Cardiothorac. Imaging 2021 3 1 e200512 10.1148/ryct.2021200512 33778661
    [Google Scholar]
  16. Batko K. Ślęzak A. The use of Big Data Analytics in healthcare. J. Big Data 2022 9 1 3 10.1186/s40537‑021‑00553‑4 35013701
    [Google Scholar]
  17. Jayatilake S.M.D.A.C. Ganegoda G.U. Involvement of Machine Learning Tools in Healthcare Decision Making. J. Healthc. Eng. 2021 2021 1 20 10.1155/2021/6679512 33575021
    [Google Scholar]
  18. Denaxas S.C. Morley K.I. Big biomedical data and cardiovascular disease research: opportunities and challenges. Eur. Heart J. Qual. Care Clin. Outcomes 2015 1 1 9 16 10.1093/ehjqcco/qcv005 29474568
    [Google Scholar]
  19. Sim I. Two Ways of Knowing: Big Data and Evidence-Based Medicine. Ann. Intern. Med. 2016 164 8 562 563 10.7326/M15‑2970 26809201
    [Google Scholar]
  20. Hemingway H. Asselbergs F.W. Danesh J. Dobson R. Maniadakis N. Maggioni A. van Thiel G.J.M. Cronin M. Brobert G. Vardas P. Anker S.D. Grobbee D.E. Denaxas S. Innovative Medicines Initiative 2nd programme, Big Data for Better Outcomes, BigData@Heart Consortium of 20 academic and industry partners including ESC Big data from electronic health records for early and late translational cardiovascular research: challenges and potential. Eur. Heart J. 2018 39 16 1481 1495 10.1093/eurheartj/ehx487 29370377
    [Google Scholar]
  21. Laney D. 3D data management: Controlling data volume, velocity and variety META Group Research Note 2001 6 70 1
    [Google Scholar]
  22. LeCun Y. Bengio Y. Hinton G. Deep learning. Nature 2015 521 7553 436 444 10.1038/nature14539 26017442
    [Google Scholar]
  23. Esteva A. Robicquet A. Ramsundar B. Kuleshov V. DePristo M. Chou K. Cui C. Corrado G. Thrun S. Dean J. A guide to deep learning in healthcare. Nat. Med. 2019 25 1 24 29 10.1038/s41591‑018‑0316‑z 30617335
    [Google Scholar]
  24. Tripathi G. Singh K. Vishwakarma D.K. Applied convolutional neural network framework for tagging healthcare systems in crowd protest environment. Math. Biosci. Eng. 2021 18 6 8727 8757 10.3934/mbe.2021431 34814320
    [Google Scholar]
  25. Silverio A. Cavallo P. De Rosa R. Galasso G. Big Health Data and Cardiovascular Diseases: A Challenge for Research, an Opportunity for Clinical Care. Front. Med. (Lausanne) 2019 6 36 10.3389/fmed.2019.00036 30873409
    [Google Scholar]
  26. Dey D. Slomka P.J. Leeson P. Comaniciu D. Shrestha S. Sengupta P.P. Marwick T.H. Artificial Intelligence in Cardiovascular Imaging. J. Am. Coll. Cardiol. 2019 73 11 1317 1335 10.1016/j.jacc.2018.12.054 30898208
    [Google Scholar]
  27. Johnson K.W. Torres Soto J. Glicksberg B.S. Shameer K. Miotto R. Ali M. Ashley E. Dudley J.T. Artificial Intelligence in Cardiology. J. Am. Coll. Cardiol. 2018 71 23 2668 2679 10.1016/j.jacc.2018.03.521 29880128
    [Google Scholar]
  28. Zamzmi G. Rajaraman S. Hsu L.Y. Sachdev V. Antani S. Real-time echocardiography image analysis and quantification of cardiac indices. Med. Image Anal. 2022 80 102438 10.1016/j.media.2022.102438 35868819
    [Google Scholar]
  29. Tison G.H. Zhang J. Delling F.N. Deo R.C. Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery. Circ. Cardiovasc. Qual. Outcomes 2019 12 9 e005289 10.1161/CIRCOUTCOMES.118.005289 31525078
    [Google Scholar]
  30. Ribeiro A.H. Ribeiro M.H. Paixão G.M.M. Oliveira D.M. Gomes P.R. Canazart J.A. Ferreira M.P.S. Andersson C.R. Macfarlane P.W. Meira W. Jr Schön T.B. Ribeiro A.L.P. Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat. Commun. 2020 11 1 1760 10.1038/s41467‑020‑15432‑4 32273514
    [Google Scholar]
  31. Madani A. Arnaout R. Mofrad M. Arnaout R. Fast and accurate view classification of echocardiograms using deep learning. NPJ Digit. Med. 2018 1 1 6 10.1038/s41746‑017‑0013‑1 30828647
    [Google Scholar]
  32. Samad M.D. Ulloa A. Wehner G.J. Jing L. Hartzel D. Good C.W. Williams B.A. Haggerty C.M. Fornwalt B.K. Predicting Survival From Large Echocardiography and Electronic Health Record Datasets. JACC Cardiovasc. Imaging 2019 12 4 681 689 10.1016/j.jcmg.2018.04.026 29909114
    [Google Scholar]
  33. Nakajima K. Kudo T. Nakata T. Kiso K. Kasai T. Taniguchi Y. Matsuo S. Momose M. Nakagawa M. Sarai M. Hida S. Tanaka H. Yokoyama K. Okuda K. Edenbrandt L. Diagnostic accuracy of an artificial neural network compared with statistical quantitation of myocardial perfusion images: a Japanese multicenter study. Eur. J. Nucl. Med. Mol. Imaging 2017 44 13 2280 2289 10.1007/s00259‑017‑3834‑x 28948350
    [Google Scholar]
  34. Su S Hu Z Lin Q Hau WK Gao Z Zhang H An artificial neural network method for lumen and media-adventitia border detection in IVUS Comput. Med. Imaging Graph. 2017 57 29 39 10.1016/j.compmedimag.2016.11.003
    [Google Scholar]
  35. Abdolmanafi A. Duong L. Dahdah N. Cheriet F. Deep feature learning for automatic tissue classification of coronary artery using optical coherence tomography. Biomed. Opt. Express 2017 8 2 1203 1220 10.1364/BOE.8.001203 28271012
    [Google Scholar]
  36. Subramanian M. Wojtusciszyn A. Favre L. Boughorbel S. Shan J. Letaief K.B. Pitteloud N. Chouchane L. Precision medicine in the era of artificial intelligence: implications in chronic disease management. J. Transl. Med. 2020 18 1 472 10.1186/s12967‑020‑02658‑5 33298113
    [Google Scholar]
  37. Currie G. Delles C. Precision Medicine and Personalized Medicine in Cardiovascular Disease. Adv. Exp. Med. Biol. 2018 1065 589 605 10.1007/978‑3‑319‑77932‑4_36 30051409
    [Google Scholar]
  38. Jung S. Song S.W. Lee S. Kim S.H. Ann S. Cheon E.J. Yi G. Choi E.Y. Lee S.H. Joo H.C. Ryu D.H. Lee S.H. Hwang G.S. Metabolic phenotyping of human atherosclerotic plaques: Metabolic alterations and their biological relevance in plaque-containing aorta. Atherosclerosis 2018 269 21 28 10.1016/j.atherosclerosis.2017.11.034 29253644
    [Google Scholar]
  39. Diller G.P. Kempny A. Babu-Narayan S.V. Henrichs M. Brida M. Uebing A. Lammers A.E. Baumgartner H. Li W. Wort S.J. Dimopoulos K. Gatzoulis M.A. Machine learning algorithms estimating prognosis and guiding therapy in adult congenital heart disease: data from a single tertiary centre including 10 019 patients. Eur. Heart J. 2019 40 13 1069 1077 10.1093/eurheartj/ehy915 30689812
    [Google Scholar]
  40. Balanescu D.V. Monlezun D.J. Donisan T. Boone D. Cervoni-Curet F. Palaskas N. Lopez-Mattei J. Kim P. Iliescu C. Balanescu S.M. A Cancer Paradox: Machine-Learning Backed Propensity-Score Analysis of Coronary Angiography Findings in Cardio-Oncology. J. Invasive Cardiol. 2019 31 1 21 26 30611123
    [Google Scholar]
  41. Kakadiaris I.A. Vrigkas M. Yen A.A. Kuznetsova T. Budoff M. Naghavi M. Machine Learning Outperforms ACC/AHA CVD Risk Calculator in MESA. J. Am. Heart Assoc. 2018 7 22 e009476 10.1161/JAHA.118.009476 30571498
    [Google Scholar]
  42. Engelhardt S. Sauerzapf S. Brčić A. Karck M. Wolf I. De Simone R. Replicated mitral valve models from real patients offer training opportunities for minimally invasive mitral valve repair. Interact. Cardiovasc. Thorac. Surg. 2019 29 1 43 50 10.1093/icvts/ivz008 30783681
    [Google Scholar]
  43. Engelhardt S. Sauerzapf S. Al-Maisary S. Karck M. Preim B. Wolf I. Elastic mitral valve silicone replica made from 3D-printable molds offer advanced surgical training. Bildverarbeitung für die Medizin 2018: Algorithmen-Systeme-Anwendungen Proceedings des Workshops vom 11 bis 13 März 2018 in Erlangen. Springer 2018
    [Google Scholar]
  44. Engelhardt S. De Simone R. Full P.M. Karck M. Wolf I. Improving surgical training phantoms by hyperrealism: deep unpaired image-to-image translation from real surgeries. 2018 21st International Conference Granada, Spain September 16-20, 2018 10.1007/978‑3‑030‑00928‑1_84
    [Google Scholar]
  45. Mazomenos E.B. Chang P.L. Rippel R.A. Rolls A. Hawkes D.J. Bicknell C.D. Desjardins A. Riga C.V. Stoyanov D. Catheter manipulation analysis for objective performance and technical skills assessment in transcatheter aortic valve implantation. Int. J. CARS 2016 11 6 1121 1131 10.1007/s11548‑016‑1391‑6 27072837
    [Google Scholar]
  46. Koshimizu H. Kojima R. Kario K. Okuno Y. Prediction of blood pressure variability using deep neural networks. Int. J. Med. Inform. 2020 136 104067 10.1016/j.ijmedinf.2019.104067 31955052
    [Google Scholar]
  47. Tsoi KKF Chan NB Yiu KKL Poon SKS Lin B Ho K Machine learning clustering for blood pressure variability applied to Systolic blood pressure intervention trial (SPRINT) and the Hong Kong community cohort Hypertension 2020 76 2 569 576
    [Google Scholar]
  48. Seligman H. Patel S.B. Alloula A. Howard J.P. Cook C.M. Ahmad Y. de Waard G.A. Pinto M.E. van de Hoef T.P. Rahman H. Kelshiker M.A. Rajkumar C.A. Foley M. Nowbar A.N. Mehta S. Toulemonde M. Tang M.X. Al-Lamee R. Sen S. Cole G. Nijjer S. Escaned J. Van Royen N. Francis D.P. Shun-Shin M.J. Petraco R. Development of artificial intelligence tools for invasive Doppler-based coronary microvascular assessment. European Heart Journal - Digital Health 2023 4 4 291 301 10.1093/ehjdh/ztad030 37538145
    [Google Scholar]
  49. Rao S.J. Iqbal S.B. Isath A. Virk H.U.H. Wang Z. Glicksberg B.S. Krittanawong C. An Update on the Use of Artificial Intelligence in Cardiovascular Medicine. Hearts 2024 5 1 91 104 10.3390/hearts5010007
    [Google Scholar]
  50. Siontis K.C. Noseworthy P.A. Attia Z.I. Friedman P.A. Artificial intelligence-enhanced electrocardiography in cardiovascular disease management. Nat. Rev. Cardiol. 2021 18 7 465 478 10.1038/s41569‑020‑00503‑2 33526938
    [Google Scholar]
  51. Fiorina L. Chemaly P. Cellier J. Said M.A. Coquard C. Younsi S. Salerno F. Horvilleur J. Lacotte J. Manenti V. Plesse A. Henry C. Lefebvre B. Artificial intelligence–based electrocardiogram analysis improves atrial arrhythmia detection from a smartwatch electrocardiogram. European Heart Journal - Digital Health 2024 5 5 535 541 10.1093/ehjdh/ztae047 39318690
    [Google Scholar]
  52. D’Agostino R.B. Sr Vasan R.S. Pencina M.J. Wolf P.A. Cobain M. Massaro J.M. Kannel W.B. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation 2008 117 6 743 753 10.1161/CIRCULATIONAHA.107.699579 18212285
    [Google Scholar]
  53. Shameer K. Johnson K.W. Glicksberg B.S. Dudley J.T. Sengupta P.P. Machine learning in cardiovascular medicine: are we there yet? Heart 2018 104 14 1156 1164 10.1136/heartjnl‑2017‑311198 29352006
    [Google Scholar]
  54. Hannun A.Y. Rajpurkar P. Haghpanahi M. Tison G.H. Bourn C. Turakhia M.P. Ng A.Y. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat. Med. 2019 25 1 65 69 10.1038/s41591‑018‑0268‑3 30617320
    [Google Scholar]
  55. Cortes C. Vapnik V. Support-vector networks. Mach. Learn. 1995 20 3 273 297 10.1007/BF00994018
    [Google Scholar]
  56. Liaw A. Wiener M. Classification and regression by randomForest. R News 2002 2 3 18 22
    [Google Scholar]
  57. Marian A.J. Braunwald E. Hypertrophic Cardiomyopathy. Circ. Res. 2017 121 7 749 770 10.1161/CIRCRESAHA.117.311059 28912181
    [Google Scholar]
  58. Siontis K.C. Liu K. Bos J.M. Attia Z.I. Cohen-Shelly M. Arruda-Olson A.M. Zanjirani Farahani N. Friedman P.A. Noseworthy P.A. Ackerman M.J. Detection of hypertrophic cardiomyopathy by an artificial intelligence electrocardiogram in children and adolescents. Int. J. Cardiol. 2021 340 42 47 10.1016/j.ijcard.2021.08.026 34419527
    [Google Scholar]
  59. Ko W.Y. Siontis K.C. Attia Z.I. Carter R.E. Kapa S. Ommen S.R. Demuth S.J. Ackerman M.J. Gersh B.J. Arruda-Olson A.M. Geske J.B. Asirvatham S.J. Lopez-Jimenez F. Nishimura R.A. Friedman P.A. Noseworthy P.A. Detection of Hypertrophic Cardiomyopathy Using a Convolutional Neural Network-Enabled Electrocardiogram. J. Am. Coll. Cardiol. 2020 75 7 722 733 10.1016/j.jacc.2019.12.030 32081280
    [Google Scholar]
  60. Virani S.S. Alonso A. Aparicio H.J. Benjamin E.J. Bittencourt M.S. Callaway C.W. Carson A.P. Chamberlain A.M. Cheng S. Delling F.N. Elkind M.S.V. Evenson K.R. Ferguson J.F. Gupta D.K. Khan S.S. Kissela B.M. Knutson K.L. Lee C.D. Lewis T.T. Liu J. Loop M.S. Lutsey P.L. Ma J. Mackey J. Martin S.S. Matchar D.B. Mussolino M.E. Navaneethan S.D. Perak A.M. Roth G.A. Samad Z. Satou G.M. Schroeder E.B. Shah S.H. Shay C.M. Stokes A. VanWagner L.B. Wang N.Y. Tsao C.W. American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee Heart Disease and Stroke Statistics—2021 Update. Circulation 2021 143 8 e254 e743 10.1161/CIR.0000000000000950 33501848
    [Google Scholar]
  61. Baxt W.G. Shofer F.S. Sites F.D. Hollander J.E. A neural network aid for the early diagnosis of cardiac ischemia in patients presenting to the emergency department with chest pain. Ann. Emerg. Med. 2002 40 6 575 583 10.1067/mem.2002.129171 12447333
    [Google Scholar]
  62. McDonagh T.A. Metra M. Adamo M. Gardner R.S. Baumbach A. Böhm M. Burri H. Butler J. Čelutkienė J. Chioncel O. Cleland J.G.F. Coats A.J.S. Crespo-Leiro M.G. Farmakis D. Gilard M. Heymans S. Hoes A.W. Jaarsma T. Jankowska E.A. Lainscak M. Lam C.S.P. Lyon A.R. McMurray J.J.V. Mebazaa A. Mindham R. Muneretto C. Francesco Piepoli M. Price S. Rosano G.M.C. Ruschitzka F. Kathrine Skibelund A. de Boer R.A. Christian Schulze P. Abdelhamid M. Aboyans V. Adamopoulos S. Anker S.D. Arbelo E. Asteggiano R. Bauersachs J. Bayes-Genis A. Borger M.A. Budts W. Cikes M. Damman K. Delgado V. Dendale P. Dilaveris P. Drexel H. Ezekowitz J. Falk V. Fauchier L. Filippatos G. Fraser A. Frey N. Gale C.P. Gustafsson F. Harris J. Iung B. Janssens S. Jessup M. Konradi A. Kotecha D. Lambrinou E. Lancellotti P. Landmesser U. Leclercq C. Lewis B.S. Leyva F. Linhart A. Løchen M-L. Lund L.H. Mancini D. Masip J. Milicic D. Mueller C. Nef H. Nielsen J-C. Neubeck L. Noutsias M. Petersen S.E. Sonia Petronio A. Ponikowski P. Prescott E. Rakisheva A. Richter D.J. Schlyakhto E. Seferovic P. Senni M. Sitges M. Sousa-Uva M. Tocchetti C.G. Touyz R.M. Tschoepe C. Waltenberger J. Adamo M. Baumbach A. Böhm M. Burri H. Čelutkienė J. Chioncel O. Cleland J.G.F. Coats A.J.S. Crespo-Leiro M.G. Farmakis D. Gardner R.S. Gilard M. Heymans S. Hoes A.W. Jaarsma T. Jankowska E.A. Lainscak M. Lam C.S.P. Lyon A.R. McMurray J.J.V. Mebazaa A. Mindham R. Muneretto C. Piepoli M.F. Price S. Rosano G.M.C. Ruschitzka F. Skibelund A.K. ESC Scientific Document Group 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur. Heart J. 2021 42 36 3599 3726 10.1093/eurheartj/ehab368 34447992
    [Google Scholar]
  63. Romiti S. Vinciguerra M. Saade W. Anso Cortajarena I. Greco E. Artificial Intelligence (AI) and Cardiovascular Diseases: An Unexpected Alliance. Cardiol. Res. Pract. 2020 2020 1 8 10.1155/2020/4972346 32676206
    [Google Scholar]
  64. Medvedofsky D. Mor-Avi V. Amzulescu M. Fernández-Golfín C. Hinojar R. Monaghan M.J. Otani K. Reiken J. Takeuchi M. Tsang W. Vanoverschelde J.L. Indrajith M. Weinert L. Zamorano J.L. Lang R.M. Three-dimensional echocardiographic quantification of the left-heart chambers using an automated adaptive analytics algorithm: multicentre validation study. Eur. Heart J. Cardiovasc. Imaging 2018 19 1 47 58 10.1093/ehjci/jew328 28159984
    [Google Scholar]
  65. Ortiz J. Ghefter C.G.M. Silva C.E.S. Sabbatini R.M.E. One-year mortality prognosis in heart failure: A neural network approach based on echocardiographic data. J. Am. Coll. Cardiol. 1995 26 7 1586 1593 10.1016/0735‑1097(95)00385‑1 7594090
    [Google Scholar]
  66. January C.T. Wann L.S. Calkins H. Chen L.Y. Cigarroa J.E. Cleveland J.C. Jr Ellinor P.T. Ezekowitz M.D. Field M.E. Furie K.L. Heidenreich P.A. Murray K.T. Shea J.B. Tracy C.M. Yancy C.W. 2019 AHA/ACC/HRS Focused Update of the 2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Rhythm Society in Collaboration With the Society of Thoracic Surgeons. Circulation 2019 140 2 e125 e151 10.1161/CIR.0000000000000665 30686041
    [Google Scholar]
  67. Raghunath S. Ulloa Cerna A.E. Jing L. vanMaanen D.P. Stough J. Hartzel D.N. Leader J.B. Kirchner H.L. Stumpe M.C. Hafez A. Nemani A. Carbonati T. Johnson K.W. Young K. Good C.W. Pfeifer J.M. Patel A.A. Delisle B.P. Alsaid A. Beer D. Haggerty C.M. Fornwalt B.K. Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network. Nat. Med. 2020 26 6 886 891 10.1038/s41591‑020‑0870‑z 32393799
    [Google Scholar]
  68. Haq I.U. Chhatwal K. Sanaka K. Xu B. Artificial Intelligence in Cardiovascular Medicine: Current Insights and Future Prospects. Vasc. Health Risk Manag. 2022 18 517 528 10.2147/VHRM.S279337 35855754
    [Google Scholar]
  69. Schepart A. Burton A. Durkin L. Fuller A. Charap E. Bhambri R. Ahmad F.S. Artificial intelligence–enabled tools in cardiovascular medicine: A survey of current use, perceptions, and challenges. Cardiovascular Digital Health Journal 2023 4 3 101 110 10.1016/j.cvdhj.2023.04.003 37351333
    [Google Scholar]
  70. Aliferis C. Simon G. Overfitting, Underfitting and General Model Overconfidence and Under-Performance Pitfalls and Best Practices in Machine Learning and AI. Artificial Intelligence and Machine Learning in Health Care and Medical Sciences: Best Practices and Pitfalls. Simon G.J. Aliferis C. Cham Springer International Publishing 2024 477 524 10.1007/978‑3‑031‑39355‑6_10
    [Google Scholar]
  71. Hassija V. Chamola V. Mahapatra A. Singal A. Goel D. Huang K. Scardapane S. Spinelli I. Mahmud M. Hussain A. Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence. Cognit. Comput. 2024 16 1 45 74 10.1007/s12559‑023‑10179‑8
    [Google Scholar]
  72. Khan B. Fatima H. Qureshi A. Kumar S. Hanan A. Hussain J. Drawbacks of Artificial Intelligence and Their Potential Solutions in the Healthcare Sector. 2023 10.1007/s44174‑023‑00063‑2
    [Google Scholar]
  73. Chen R.J. Wang J.J. Williamson D.F.K. Chen T.Y. Lipkova J. Lu M.Y. Sahai S. Mahmood F. Algorithmic fairness in artificial intelligence for medicine and healthcare. Nat. Biomed. Eng. 2023 7 6 719 742 10.1038/s41551‑023‑01056‑8 37380750
    [Google Scholar]
  74. Bengani V. Hybrid learning systems: Integrating traditional machine learning with deep learning techniques Thesis for: BCA 2024
    [Google Scholar]
  75. Mohammadi S. Balador A. Sinaei S. Flammini F. Balancing privacy and performance in federated learning: A systematic literature review on methods and metrics. J. Parallel Distrib. Comput. 2024 192 104918 10.1016/j.jpdc.2024.104918
    [Google Scholar]
  76. Li N. Wang Y. Wei P. Min Y. Yu M. Zhou G. Yuan G. Sun J. Dai H. Zhou E. He W. Sheng M. Gao K. Zheng M. Sun W. Zhou D. Zhang L. Causal effects of specific gut microbiota on chronic kidney diseases and renal function—a two-sample mendelian randomization study. Nutrients 2023 15 2 360 10.3390/nu15020360 36678231
    [Google Scholar]
  77. Zhang J. Gajjala S. Agrawal P. Tison G.H. Hallock L.A. Beussink-Nelson L. Lassen M.H. Fan E. Aras M.A. Jordan C. Fleischmann K.E. Melisko M. Qasim A. Shah S.J. Bajcsy R. Deo R.C. Fully automated echocardiogram interpretation in clinical practice: Feasibility and diagnostic accuracy. Circulation 2018 138 16 1623 1635 10.1161/CIRCULATIONAHA.118.034338 30354459
    [Google Scholar]
  78. Olveres J. González G. Torres F. Moreno-Tagle J.C. Carbajal-Degante E. Valencia-Rodríguez A. Méndez-Sánchez N. Escalante-Ramírez B. What is new in computer vision and artificial intelligence in medical image analysis applications. Quant. Imaging Med. Surg. 2021 11 8 3830 3853 10.21037/qims‑20‑1151 34341753
    [Google Scholar]
  79. Akkus Z. Aly Y.H. Attia I.Z. Lopez-Jimenez F. Arruda-Olson A.M. Pellikka P.A. Pislaru S.V. Kane G.C. Friedman P.A. Oh J.K. Artificial intelligence (AI)-empowered echocardiography interpretation: A state-of-the-art review. J. Clin. Med. 2021 10 7 1391 10.3390/jcm10071391 33808513
    [Google Scholar]
  80. Park J.H. Cho H.E. Kim J.H. Wall M.M. Stern Y. Lim H. Yoo S. Kim H.S. Cha J. Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data. NPJ Digit. Med. 2020 3 1 46 10.1038/s41746‑020‑0256‑0 32258428
    [Google Scholar]
  81. Hall A. Mitchell A.R.J. Wood L. Holland C. Effectiveness of a single lead AliveCor electrocardiogram application for the screening of atrial fibrillation. Medicine 2020 99 30 e21388 10.1097/MD.0000000000021388 32791751
    [Google Scholar]
  82. Bahrami M. Forouzanfar M. Sleep apnea detection from single-lead ECG: A comprehensive analysis of machine learning and deep learning algorithms. IEEE Trans. Instrum. Meas. 2022 71 1 11 10.1109/TIM.2022.3151947
    [Google Scholar]
  83. Sufyan M. Shokat Z. Ashfaq U.A. Artificial intelligence in cancer diagnosis and therapy: Current status and future perspective. Comput. Biol. Med. 2023 165 107356 10.1016/j.compbiomed.2023.107356 37688994
    [Google Scholar]
  84. Sardar P Abbott JD Kundu A Aronow HD Granada JF Giri J Impact of Artificial Intelligence on interventional cardiology: From decision-making aid to advanced interventional procedure assistance JACC Cardiovasc Interv. 2019 12 14 1293 1303
    [Google Scholar]
  85. Singh L.K. Khanna M. Singh R. Artificial intelligence based medical decision support system for early and accurate breast cancer prediction. Adv. Eng. Softw. 2023 175 103338 10.1016/j.advengsoft.2022.103338
    [Google Scholar]
  86. Kayode S. Aston D. Groundbreaking Utilizations of Computerized reasoning in Medical services. Improving Diagnostics, Therapy, and Patient Consideration 2023
    [Google Scholar]
  87. Kocakoç I.D. The Role of Artificial Intelligence in Health Care. The Impact of Artificial Intelligence on Governance, Economics and Finance. Springer 2022 Vol. 2 189 206
    [Google Scholar]
  88. Kaur S. Singla J. Nkenyereye L. Jha S. Prashar D. Joshi G.P. El-Sappagh S. Islam M.S. Islam S.M.R. Medical diagnostic systems using artificial intelligence (ai) algorithms: Principles and perspectives. IEEE Access 2020 8 228049 228069 10.1109/ACCESS.2020.3042273
    [Google Scholar]
  89. Harvey N.C.W. McCloskey E.V. Mitchell P.J. Dawson-Hughes B. Pierroz D.D. Reginster J.Y. Rizzoli R. Cooper C. Kanis J.A. Mind the (treatment) gap: a global perspective on current and future strategies for prevention of fragility fractures. Osteoporos. Int. 2017 28 5 1507 1529 10.1007/s00198‑016‑3894‑y 28175979
    [Google Scholar]
  90. Barrett M. Boyne J. Brandts J. Brunner-La Rocca H.P. De Maesschalck L. De Wit K. Dixon L. Eurlings C. Fitzsimons D. Golubnitschaja O. Hageman A. Heemskerk F. Hintzen A. Helms T.M. Hill L. Hoedemakers T. Marx N. McDonald K. Mertens M. Müller-Wieland D. Palant A. Piesk J. Pomazanskyi A. Ramaekers J. Ruff P. Schütt K. Shekhawat Y. Ski C.F. Thompson D.R. Tsirkin A. van der Mierden K. Watson C. Zippel-Schultz B. Artificial intelligence supported patient self-care in chronic heart failure: a paradigm shift from reactive to predictive, preventive and personalised care. EPMA J. 2019 10 4 445 464 10.1007/s13167‑019‑00188‑9 31832118
    [Google Scholar]
  91. Vora L.K. Gholap A.D. Jetha K. Thakur R.R.S. Solanki H.K. Chavda V.P. Artificial intelligence in pharmaceutical technology and drug delivery design. Pharmaceutics 2023 15 7 1916 10.3390/pharmaceutics15071916 37514102
    [Google Scholar]
  92. Chan P.Y. Ryan N.P. Chen D. McNeil J. Hopper I. Novel wearable and contactless heart rate, respiratory rate, and oxygen saturation monitoring devices: a systematic review and meta‐analysis. Anaesthesia 2022 77 11 1268 1280 10.1111/anae.15834 35947876
    [Google Scholar]
  93. Koul A. Bawa R.K. Kumar Y. Artificial intelligence techniques to predict the airway disorders illness: a systematic review. Arch. Comput. Methods Eng. 2023 30 2 831 864 10.1007/s11831‑022‑09818‑4 36189431
    [Google Scholar]
  94. Lopez-Jimenez F. Attia Z. Arruda-Olson A.M. Carter R. Chareonthaitawee P. Jouni H. Artificial intelligence in cardiology: present and future. Mayo Clinic Proceedings. Elsevier 2020
    [Google Scholar]
  95. Pontone G. Weir-McCall J.R. Baggiano A. Del Torto A. Fusini L. Guglielmo M. Muscogiuri G. Guaricci A.I. Andreini D. Patel M. Nieman K. Akasaka T. Rogers C. Nørgaard B.L. Bax J. Raff G.L. Chinnaiyan K. Berman D. Fairbairn T. Koweek L.H. Leipsic J. Determinants of rejection rate for coronary CT angiography fractional flow reserve analysis. Radiology 2019 292 3 597 605 10.1148/radiol.2019182673 31335283
    [Google Scholar]
  96. Vercauteren T. Unberath M. Padoy N. Navab N. Cai4cai: the rise of contextual artificial intelligence in computer-assisted interventions. Proc. IEEE 2020 108 1 198 214 10.1109/JPROC.2019.2946993 31920208
    [Google Scholar]
  97. Keshavarzi Arshadi A Webb J Salem M Cruz E Calad-Thomson S Ghadirian N Artificial Intelligence for COVID-19 drug discovery and vaccine development Front. Artif. Intell. 2020 3 65
    [Google Scholar]
  98. Fiske A. Henningsen P. Buyx A. Your robot therapist will see you now: Ethical implications of embodied artificial intelligence in psychiatry, psychology, and psychotherapy. J. Med. Internet Res. 2019 21 5 e13216 10.2196/13216 31094356
    [Google Scholar]
  99. Kasula B.Y. Framework development for artificial intelligence integration in healthcare: optimizing patient care and operational efficiency. Transactions on Latest Trends in IoT. 2023 6 6 77 83
    [Google Scholar]
  100. Lee HHY Scott D Overview of maintenance strategy, acceptable maintenance standard and resources from a building maintenance operation perspective J Build Apprais 2009 4 269 278 10.1057/jba.2008.46
    [Google Scholar]
  101. Mohsen F. Al-Saadi B. Abdi N. Khan S. Shah Z. Artificial intelligence-based methods for precision cardiovascular medicine. J. Pers. Med. 2023 13 8 1268 10.3390/jpm13081268 37623518
    [Google Scholar]
  102. Lee S. Chu Y. Ryu J. Park Y.J. Yang S. Koh S.B. Artificial intelligence for detection of cardiovascular-related diseases from wearable devices: A systematic review and meta-analysis. Yonsei Med. J. 2022 63 Suppl. S93 S107 10.3349/ymj.2022.63.S93 35040610
    [Google Scholar]
  103. Huang J.D. Wang J. Ramsey E. Leavey G. Chico T.J.A. Condell J. Applying artificial intelligence to wearable sensor data to diagnose and predict cardiovascular disease: A review. Sensors 2022 22 20 8002 10.3390/s22208002 36298352
    [Google Scholar]
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  • Article Type:
    Review Article
Keywords: heart failure ; Artificial intelligence ; CVDs ; deep learning ; big data ; ANN ; CNN ; machine learning
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