Innovation of Artifcial Intelligence for the Diagnosis of Cardiovascular Disease
Keywords:
Cardiovascular diseases, Artificial intelligence, DiagnosisAbstract
Cardiovascular diseases (CVDs) is classified as a group of chronic, non-communicable diseases affecting the cardiovascular system, leading to high mortality rates worldwide, including in Thailand. Delayed diagnoses and non-specialist medical personnel remain major issues in the public health system. The development of artificial intelligence (Al) for managing medical conditions is currently gaining popularity. Developing and applying Al to accurately diagnose cardiovascular diseases from patient data enables significant improvements in disease diagnosis, treatment, and management. This article presented educational information and the application of Al in managing cardiovascular disease. It provides an overview of cardiovascular disease and artificial intelligence (AI) technology, including general information about disease and Al, the role of Al in disease diagnosis using medical imaging and treatment, demand of usage, problems, and obstacles, as well as reports on Al studies in Thailand. The information was searched on Google Scholar for the terms "Cardiovascular diseases" and "Artificial intelligence or Al," and the data and research articles from 2018 to 2023 were randomly selected. This review may be useful for the development of public health systems, particularly in improving diagnostic processes and enhancing the efficiency of disease treatment.
References
WHO. Cardiovascular diseases (CVDs) [Internet]. [cited 2024 Jun 15]. Available from; https;//www.who.int/ news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)
Baashar Y, Alkawsi G, Alhussian H, Capretz LF, Alwadain A, Alkahtani AA, et al. Effectiveness of artificial intelligence models for cardiovascular disease prediction; network meta-analysis. Comput Intell Neurosci 2022;2022;5849995.
Wanitchatchawan W, Tadadej C, Pongpirul K. Effect of a personalized cardiovascular risk score report on employee smoking behavior in a private hospital. THJPH 2022;52(1);69-78.
Public Sector Development Group. Cardiovascular disease [Internet]. 2024 [cited 2024 Apr 7]. Available from; https;//medinfo.dms.go.th/public-health/sta_report.php
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.
Zhao J, Kelly M, Bain C, Seubsman SA, Sleigh A; Thai Cohort Study Team. Risk factors for cardiovascular disease mortality among 86866 members of the Thai Cohort Study, 2005-2010. Glob J Health Sci 2015;7(1);107-14.
Mathur P, Srivastava S, Xu X, Mehta JL. Artificial intelligence, machine learning, and cardiovascular disease. Clin Med Insights Cardiol 2020;14;117954682092740.
Boccuto F, De Rosa S, Torella D, Veltri P, Guzzi PH. Will artificial intelligence provide answers to current gaps and needs in chronic heart failure? Applied sciences 2023;13(13);7663.
Jantongpoon J, Laisam P, Sae-Wong S, Bonrasri P. A study of urban expansion with random forest techniques: a case study of Mueang Songkhla district, Songkhla province. In: Damrongwiriyanupap N, editor. The 27th National Convention on Civil Engineering; 2022 August 24-25; The Heritage Chiang Rai Hotel and Convention. Phayao: UP Office of Publishing and Printing; 2022. p. SGI05-1.
Chaopradith N, Lekcharoen S. Development of Thai language profanity investigation model for online media using data mining technique. In: Ourairat A, editor. The 12th RSU National Graduate Research Conference; 2017 August 17; Rangsit University. Pathumthani: RSU Office of Publishing and Printing; 2017. p. 1432-41.
Pairsuwan S, Punpocha S, Puangkird B. Foreign exchange rates forecasting using deep learning. In: Ourairat A, editor. The 15th RSU National Graduate Research Conference; 2020 August 13; Rangsit University. Pathum Thani: RSU Office of Publishing and Printing; 2020. p. 2606-17.
Cheewaprakobkit P. Improving the performance of an image classification with convolutional neural net- work model by using image augmentations technique. Journal of Engineering and Digital Technology 2019;7(1);59-64.
Kalra A, Lowe A, Al-Jumaily A. Critical review of electrocardiography measurement systems and technology. Measurement Science and Technology 2018;30(1);012001.
Chou CC, Liu ZY, Chang PC, Liu HT, Wo HT, Lee WC, et al. Comparing artificial intelligence-enabled electrocardiogram models in identifying left atrium enlargement and long-term cardiovascular risk. Can J Cardiol 2024;40(4);585-94.
Valsaraj A, Kalmady SV, Sharma V, Frost M, Sun W, Sepehrvand N, et al. Development and validation of echocardiography-based machine-learning models to predict mortality. EBioMedicine 2023;90;104479.
Liu B, Chang H, Yang D, Yang F, Wang Q, Deng Y, et al. A deep learning framework assisted echocardiography with diagnosis, lesion localization, phenogrouping heterogeneous disease, and anomaly detection. Sci Rep 2023;13(1);3.
Choi AD, Marques H, Kumar V, Griffin WF, Rahban H, Karlsberg RP, et al. CT evaluation by artificial intelligence for atherosclerosis, stenosis and vascular morphology (CLARIFY); a multi-center, international study. J Cardiovasc Comput Tomogr 2021;15(6);470-6.
Chen D, Bhopalwala H, Dewaswala N, Arunachalam SP, Enayati M, Farahani NZ, et al. Deep neural network for cardiac magnetic resonance image segmentation. J Imaging 2022;8(5);149.
Huang J, Ferreira PF, Wang L, Wu Y, Aviles-Rivero Al, Schönlieb CB, et al. Deep learning-based diffusion tensor cardiac magnetic resonance reconstruction; a comparison study. Sci Rep 2024;14(1);5658.
Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T. Artificial intelligence in precision cardiovascular medicine. J Am Coll Cardiol 2017;69(21);2657-64.
Barrett M, Boyne J, Brandts J, Brunner-La Rocca HP, De Maesschalck L, De Wit K, et al. 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);445-64.
Vandenberk B, Chew DS, Prasana D, Gupta S, Exner DV. Successes and challenges of artificial intelligence in cardiology. Frontiers in Digital Health 2023;5;1201392.
Nai-Arun N. The performance comparison of cardiovascular risk prediction models using data mining algorithms. scimsu 2021;40(2);137-47.
Taerakul T, Narawong T, Sisai S, Worratammongkol K. Enhancing coronary artery disease prognosis with artificial intelligence; a study on radionuclide myocardial perfusion imaging. J DMS 2023;48(3);38-45.
Maingao B, Khamkon P, Srikaew K. Analyzes the risk of cardiovascular disease. The Journal of Industrial Technology Suan Sunandha Rajabhat University 2018;5(1);55-65.
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