This study analyzes the research status and trends concerning the global application of AI in heart diseases within the past 36 years, to aid researchers navigate more studies in the future. We acquired publications from to 1986–2021 from PubMed using terms from Medical Subject Headings (MeSH) thesaurus. Subsequently, we filtered the type and language of publications in Web of Science and analyzed the data for descriptive statistics, fitting curves, and CiteSpace for visualization analysis.A total of 2038 records related to the application of AI in heart diseases were imported into CiteSpace. There has been a growing number of publications over the past 36 years roughly. Two author clusters focused on application of AI in heart diseases for clinical practice and data-driven medicine.And USA produced the most academic achievements with high impact. Machine learning, neural network, classification, diagnosis, heart failure, atrial fibrillation, and myocardial infarction have become the principal research hotspots and trends in recent years. Generally, AI techniques are increasingly applied to heart diseases in areas ranging from studies on medical data to clinical practice-related robotics-assist. Compared with conventional approaches, AI has been outperforming for heart diseases in terms of surgery operation, identification, detection, prediction, classification, and risk stratification. It has the potential to make healthcare feasible, safe, and accessible.

Qian Xu. Application of artificial intelligence in heart diseases: A bibliometrics review based on CiteSpace analysis. Academic Journal of Medicine & Health Sciences (2023) Vol. 4, Issue 12: 10-19. https://doi.org/10.25236/AJMHS.2023.041202.

[1] Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism[J].2017, 69S: S36-S40. 

[2] Lee HC, Park JS, Choe JC, et al.  Prediction of 1-Year Mortality from Acute Myocardial Infarction Using Machine Learning[J].  Am J Cardiol,2020 ,133:23-31.

[3]  D'Ascenzo F, De Filippo O, Gallone G, et al. Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets[J]. Lancet. 2021,397:199-207.

[4] Jian Z, Wang X, Zhang J,et al. Diagnosis of left ventricular hypertrophy using convolutional neural network[J]. BMC Med Inform Decis Mak. 2020,20(1):243.

[5] Synnestvedt MB, Chen C, Holmes JH. CiteSpace II: visualization and knowledge discovery in bibliographic databases[J]. AMIA Annu Symp Proc,2005,2005:724-728.

[6] Xiong HY, Zhang ZJ, Wang XQ. Bibliometric Analysis of Research on the Comorbidity of Pain and Inflammation [J]. Pain Res Manag,2021,2021:6655211.

[7] Liu S, Sun YP, Gao XL, et al. Knowledge domain and emerging trends in Alzheimer's disease: a scientometric review based on CiteSpace analysis[J]. Neural Regen Res,2019, 14:1643-1650. 

[8] Wu Z, Xue R, Shao M. Knowledge graph analysis and visualization of AI technology applied in COVID-19[J]. Environ Sci Pollut Res Int,2021, 2:1-13. 

[9] Falk V, Walther T, Autschbach R, et al. Robot-assisted minimally invasive solo mitral valve operation[J]. J Thorac Cardiovasc Surg,1998,115:470-471. 

[10] Chitwood WR Jr. Video-assisted and robotic mitral valve surgery: toward an endoscopic surgery[J]. Semin Thorac Cardiovasc Surg. 1999,11(3):194-205.

[11] Damiano RJ Jr, Tabaie HA, Mack MJ, et al. Initial prospective multicenter clinical trial of robotically-assisted coronary artery bypass grafting[J]. Ann Thorac Surg,2001, 72: 1263-1268.

[12] Rillig A, Lin T, Schmidt B, et al, Experience matters: long-term results of pulmonary vein isolation using a robotic navigation system for the treatment of paroxysmal atrial fibrillation [J]. Clin Res Cardiol, 2016, 105:106-116. 

[13] Boman K, Olofsson M, Berggren P, et al. Robot-assisted remote echocardiographic examination and teleconsultation: a randomized comparison of time to diagnosis with standard of care referral approach [J]. JACC Cardiovasc Imaging,2014,7:799-803.  

[14] Akay M, Welkowitz W. Acoustical detection of coronary occlusions using neural networks[J]. J Biomed Eng, 1993,15:469-473. 

[15] Chong CF, Li YC, Wang TL, et al. Stratification of adverse outcomes by preoperative risk factors in coronary artery bypass graft patients: an artificial neural network prediction model[J]. AMIA Annu Symp Proc, 2003,2003:160-164.

[16] Polak MJ, Zhou SH, Rautaharju PM,et al. Using automated analysis of the resting twelve-lead ECG to identify patients at risk of developing transient myocardial ischaemia--an application of an adaptive logic network[J]. Physiol Meas,1997,18:317-325. 

[17] Yang S, Yamauchi K, Nonokawa M, et al. Use of an artificial neural network to differentiate between ECGs with IRBBB patterns of atrial septal defect and healthy subjects[J]. Med Inform Internet Med, 2002, 27:49-58. 

[18] Argyro Kampouraki; George Manis; Christophoros Nikou. Heartbeat Time Series Classification With Support Vector Machines[J]. IEEE Transactions on Information Technology in Biomedicine, 2008, 13: 512-518.

[19] Li Q, Rajagopalan C, Clifford GD. A machine learning approach to multi-level ECG signal quality classification [J]. Comput Methods Programs Biomed,2014,117:435-447. 

[20] Park JH, Shin SD, Song KJ, et al. Prediction of good neurological recovery after out-of-hospital cardiac arrest: A machine learning analysis[J]. Resuscitation,2019,142:127-135.

[21] VanHouten JP, Starmer JM, Lorenzi NM, et al. Machine learning for risk prediction of acute coronary syndrome[J]. AMIA Annu Symp Proc,2014,2014:1940-1949. 

[22] Seki T, Tamura T, Suzuki M. Outcome prediction of out-of-hospital cardiac arrest with presumed cardiac aetiology using an advanced machine learning technique[J]. Resuscitation,2019,141: 128-135. 

[23] Shah AD, Bailey E, Williams T, et al. Natural language processing for disease phenotyping in UK primary care records for research: a pilot study in myocardial infarction and death [J]. J Biomed Semantics, 2019, 10:20.

[24] Hsu PY, Cheng CK. Arrhythmia Classification using Deep Learning and Machine Learning with Features Extracted from Waveform-based Signal Processing[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2020, 2020: 292-295. 

[25] Matsumoto T, Kodera S, Shinohara H, et al. Diagnosing Heart Failure from Chest X-Ray Images Using Deep Learning[J]. Int Heart J,2020,61:781-786. 

[26] Ashfaq A, Sant'Anna A, Lingman M, et al. Readmission prediction using deep learning on electronic health records[J]. J Biomed Inform,2019,97:103256. 

[27] Que Q, Tang Z, Wang R, et al. CardioXNet: Automated Detection for Cardiomegaly Based on Deep Learning [J]. Annu Int Conf IEEE Eng Med Biol Soc,2018,2018:612-615. 

[28] Ouyang N, Yamauchi K. Using a neural network to diagnose the hypertrophic portions of hypertrophic cardiomyopathy [J]. MD Comput,1998,15:106-109.

[29] Neagoe V, Iatan I, Grunwald S. A neuro-fuzzy approach to classification of ECG signals for ischemic heart disease diagnosis[J]. AMIA Annu Symp Proc,2003,2003:494-498.

[30] 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[J]. BMC Med Inform Decis Mak,2018,18:44.

[31] Fernandes M, Mendes R, Vieira SM, et al. Risk of mortality and cardiopulmonary arrest in critical patients presenting to the emergency department using machine learning and natural language processing [J]. PLoS One,2020,15:e0230876.  

[32] Carneiro G, Nascimento JC, Freitas A. The segmentation of the left ventricle of the heart from ultrasound data using deep learning architectures and derivative-based search methods[J]. IEEE Trans Image Process, 2012, 21: 968-982. 

[33] Hata E, Seo C, Nakayama M, et al. Classification of Aortic Stenosis Using ECG by Deep Learning and its Analysis Using Grad-CAM [J]. Annu Int Conf IEEE Eng Med Biol Soc,2020,2020:1548-1551.

[34] Poh MZ, Poh YC, Chan PH,et al. Diagnostic assessment of a deep learning system for detecting atrial fibrillation in pulse waveforms[J]. Heart, 2018, 104:1921-1928.