Artificial Intelligence in Cardiology

1 Kulkarni P, Mahadevappa M, Chilakamarri S. The Emergence of Artificial Intelligence in Cardiology: Current and Future Applications. Curr Cardiol Rev. 2022;18(3):e191121198124. Search in Google Scholar

2 Nakamura T, Sasano T. Artificial intelligence and cardiology: Current status and perspective. J Cardiol. 2022;79(3):326-333. Search in Google Scholar

3 Shehab M, Abualigah L, Shambour Q, et al. Machine learning in medical applications: A review of state-of-theart methods. Comput Biol Med. 2022;145:105458. Search in Google Scholar

4 Davis A, Billick K, Horton K, et al. Artificial Intelligence and Echocardiography: A Primer for Cardiac Sonographers. J Am Soc Echocardiogr. 2020;33(9):1061-1066. Search in Google Scholar

5 Gupta MD, Kunal S, Girish MP, Gupta A, Yadav R. Artificial intelligence in cardiology: The past, present and future. Indian Heart J. 2022;74(4):265-269. Search in Google Scholar

6 Attia ZI, Harmon DM, Behr ER, Friedman PA. Application of artificial intelligence to the electrocardiogram. Eur Heart J. 2021;42(46):4717-4730. Search in Google Scholar

7 Pandey A, Kagiyama N, Yanamala N, et al. Deep-Learning Models for the Echocardiographic Assessment of Diastolic Dysfunction. JACC Cardiovasc Imaging. 2021;14(10):1887-1900. Search in Google Scholar

8 Alsharqi M, Woodward WJ, Mumith JA, Markham DC, Upton R, Leeson P. Artificial intelligence and echocardiography. Echo Res Pract. 2018;5(4):R115-R125. Search in Google Scholar

9 Hwang IC, Choi D, Choi YJ, et al. Differential diagnosis of common etiologies of left ventricular hypertrophy using a hybrid CNN-LSTM model. Sci Rep. 2022;12(1):20998. Search in Google Scholar

10 Nedadur R, Wang B, Tsang W. Artificial intelligence for the echocardiographic assessment of valvular heart disease. Heart. 2022;108(20):1592-1599. Search in Google Scholar

11 Yang F, Chen X, Lin X, et al. Automated Analysis of Doppler Echocardiographic Videos as a Screening Tool for Valvular Heart Diseases. JACC Cardiovasc Imaging. 2022;15(4):551-563. Search in Google Scholar

12 Sengupta PP, Shrestha S, Kagiyama N, et al. A Machine-Learning Framework to Identify Distinct Phenotypes of Aortic Stenosis Severity. JACC Cardiovasc Imaging. 2021;14(9):1707-1720. Search in Google Scholar

13 Sehly A, Jaltotage B, He A, Maiorana A, Ihdayhid AR, Rajwani A et al. Artificial Intelligence in Echocardiography: The Time is Now. Reviews in Cardiovascular Medicine. 2022 Aug;23(8):256. Search in Google Scholar

14 Yoon YE, Kim S, Chang HJ. Artificial Intelligence and Echocardiography. J Cardiovasc Imaging. 2021;29(3):193-204. Search in Google Scholar

15 Zhang Z, Zhu Y, Liu M, et al. Artificial Intelligence-Enhanced Echocardiography for Systolic Function Assessment. J Clin Med. 2022;11(10):2893. Search in Google Scholar

16 Shah AM, Cikes M, Prasad N, et al. Echocardiographic Features of Patients With Heart Failure and Preserved Left Ventricular Ejection Fraction. J Am Coll Cardiol. 2019;74(23):2858-2873. Search in Google Scholar

17 Vafaeezadeh M, Behnam H, Hosseinsabet A, Gifani P. A deep learning approach for the automatic recognition of prosthetic mitral valve in echocardiographic images. Comput Biol Med. 2021;133:104388. Search in Google Scholar

18 Fletcher AJ, Lapidaire W, Leeson P. Machine Learning Augmented Echocardiography for Diastolic Function Assessment. Front Cardiovasc Med. 2021;8:711611. Search in Google Scholar