Advances in the Application of Artificial Intelligence in Heart Disease Detection
Journal: Journal of Clinical Medicine Research DOI: 10.32629/jcmr.v6i4.4559
Abstract
Heart disease is one of the leading causes of death and disability worldwide. Early detection and accurate diagnosis are crucial for improving clinical outcomes. In recent years, the application of artificial intelligence (AI) in medical imaging and clinical data analysis has provided new strategies for the automatic detection of cardiac diseases. This review summarizes recent progress in AI-based approaches for detecting aortic stenosis, congenital heart disease, rheumatic heart disease, left ventricular hypertrophy, ventricular function and wall motion abnormalities, and heart failure.
Keywords
Heart disease detection; Echocardiography; Artificial intelligence
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[2]Huang Z, Wessler B S, Hughes M C. Detecting heart disease from multi-view ultrasound images via supervised attention multiple instance learning[C]//Machine Learning for Healthcare Conference. PMLR, 2023: 285-307.
[3]Yang F, Chen X, Lin X, et al. Automated analysis of Doppler echocardiographic videos as a screening tool for valvular heart diseases[J]. Cardiovascular Imaging, 2022, 15(4): 551-563.
[4]Nedadur R, Wang B, Tsang W. Artificial intelligence for the echocardiographic assessment of valvular heart disease[J]. Heart, 2022, 108(20): 1592-1599.
[5]Jiang X, Yu J, Ye J, et al. A deep learning-based method for pediatric congenital heart disease detection with seven standard views in echocardiography[J]. World Journal of Pediatric Surgery, 2023, 6(3): e000580.
[6]Rahman T, Al-Ruweidi M K A A, Sumon M S I, et al. Deep learning technique for congenital heart disease detection using stacking-based CNN-LSTM models from fetal echocardiogram: a pilot study[J]. IEEE Access, 2023, 11: 110375-110390.
[7]Cheng M, Wang J, Liu X, et al. Development and validation of a deep-learning network for detecting congenital heart disease from multi-view multi-modal transthoracic echocardiograms[J]. Research, 2024, 7: 0319.
[8]Day T G, Kainz B, Hajnal J, et al. Artificial intelligence, fetal echocardiography, and congenital heart disease[J]. 2021.
[9]Rwebembera J, Marangou J, Mwita J C, et al. 2023 World Heart Federation guidelines for the echocardiographic diagnosis of rheumatic heart disease[J]. Nature Reviews Cardiology, 2024, 21(4): 250-263.
[10]Brown K, Roshanitabrizi P, Rwebembera J, et al. Using artificial intelligence for rheumatic heart disease detection by echocardiography: Focus on mitral regurgitation[J]. Journal of the American Heart Association, 2024, 13(2): e031257.
[11]Martins J F B S, Nascimento E R, Nascimento B R, et al. Towards automatic diagnosis of rheumatic heart disease on echocardiographic exams through video-based deep learning[J]. Journal of the American Medical Informatics Association, 2021, 28(9): 1834-1842.
[12]Providência R, Aali G, Zhu F, et al. Handheld echocardiography for the screening and diagnosis of rheumatic heart disease: a systematic review to inform WHO guidelines[J]. The Lancet Global Health, 2024, 12(6): e983-e994.
[13]Francis J R, Fairhurst H, Yan J, et al. Abbreviated echocardiographic screening for rheumatic heart disease by nonexperts with and without offsite expert review: a diagnostic accuracy study[J]. Journal of the American Society of Echocardiography, 2023, 36(7): 733-745.
[14]Pandian N G, Kim J K, Arias-Godinez J A, et al. Recommendations for the use of echocardiography in the evaluation of rheumatic heart disease: a report from the American Society of Echocardiography[J]. Journal of the American Society of Echocardiography, 2023, 36(1): 3-28.
[15]Rwebembera J, Marangou J, Mwita J C, et al. 2023 World Heart Federation guidelines for the echocardiographic diagnosis of rheumatic heart disease[J]. Nature Reviews Cardiology, 2024, 21(4): 250-263.
[16]Firima E, Gonzalez L, Manthabiseng M, et al. Implementing focused echocardiography and AI-supported analysis in a population-based survey in Lesotho: implications for community-based cardiovascular disease care models[J]. Hypertension Research, 2024, 47(3): 708-713.
[17]Alhussein M, Liu M X. Deep Learning in Echocardiography for Enhanced Detection of Left Ventricular Function and Wall Motion Abnormalities[J]. Ultrasound in Medicine & Biology, 2025.
[18]Bhave S, Rodriguez V, Poterucha T, et al. Deep learning to detect left ventricular structural abnormalities in chest X-rays[J]. European heart journal, 2024, 45(22): 2002-2012.
[19]Kasim S, Tang J, Malek S, et al. Enhancing reginal wall abnormality detection accuracy: Integrating machine learning, optical flow algorithms, and temporal convolutional networks in multi-view echocardiography[J]. Plos one, 2024, 19(9): e0310107.
[20]Bhave S, Rodriguez V, Poterucha T, et al. Deep learning to detect left ventricular structural abnormalities in chest X-rays[J]. European heart journal, 2024, 45(22): 2002-2012.
[21]Petmezas G, Papageorgiou V E, Vassilikos V, et al. Enhanced heart failure mortality prediction through model-independent hybrid feature selection and explainable machine learning[J]. Journal of Biomedical Informatics, 2025, 163: 104800.
[22]Myhre P L, Hung C L, Frost M J, et al. External validation of a deep learning algorithm for automated echocardiographic strain measurements[J]. European Heart Journal-Digital Health, 2024, 5(1): 60-68.
[23]Udoy I A, Hassan O. AI-Driven Technology in Heart Failure Detection and Diagnosis: A Review of the Advancement in Personalized Healthcare[J]. Symmetry, 2025, 17(3): 469.
[24]He XX, Cai YP. Current research status of explainability in artificial intelligence and evaluation of its application effects in medical fields [J]. Journal of Integration Technology, 2024, 13(6): 76-89.
Copyright © 2025 Bokai Yang, Jikui Liu
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