Citation Information :
Choudhury M. Role of Artificial Intelligence in Echocardiography: A Narrative Review. J Perioper Echocardiogr 2021; 9 (2):29-32.
Echocardiography has been in wide use over the past decade in most cardiac diseases for both diagnostic and prognostic purposes. This is because of its portability, high temporal resolution, absence of radiation, and low costs. It is also true that image analysis, including quantification and reporting, has become extensively resource-sensitive and time-consuming. The recent advent of artificial intelligence (AI) technology has created an environment in which AI confers the ability for automation, image acquisition, analysis, and interpretation in a more predictive manner. In spite of a few challenges that are yet to be overcome, AI in echocardiography seems to be promising because of its benefits obtained from automated analysis and interpretation.
Knuuti J, Wijns W, Saraste A, et al. 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes. Eur Heart J 2020;41(3):407–477. DOI: 10.1093/eurheartj/ehz425
Baumgartner H, Falk V, Bax JJ, et al. 2017 ESC/EACTS guidelines for the management of valvular heart disease. Rev Esp Cardiol (Engl Ed) 2018;71(2):110. DOI: 10.1016/j.rec.2017.12.013
Steeds RP, Garbi M, Cardim N, et al. EACVI appropriateness criteria for the use of transthoracic echocardiography in adults: a report of literature and current practice review. Eur Heart J Cardiovasc Imaging 2017;18(11):1191–1204. DOI: 10.1093/ehjci/jew333
Samuel AL. Some studies in machine learning using the game of checkers. BMJ 1959;3(3):210–229. DOI: 10.1147/rd.33.0210
He J, Baxter SL, Xu J, et al. The practical implementation of artificial intelligence technologies in medicine. Nat Med 2019;25(1):30–36. DOI: 10.1038/s41591-018-0307-0
Dey D, Slomka PJ, Leeson P, et al. Artificial intelligence in cardiovascular imaging. J Am Coll Cardiol 2019;73(11):1317–1335. DOI: 10.1016/j.jacc.2018.12.054
Asch FM, Poilvert N, Abraham T, et al. Automated echocardiographic quantification of left ventricular ejection fraction without volume measurements using a machine learning algorithm mimicking a human expert. Circ Cardiovasc Imaging 2019;12(9): e009303. DOI: 10.1161/CIRCIMAGING.119.009303
Alsharqi M, Woodward WJ, Mumith JA, et al. Artificial intelligence and echocardiography. Echo Res Pract 2018;5(4):R115–R125. DOI: 10.1530/ERP-18-0056
Sengupta PP, Adjeroh DA. Will artificial intelligence replace the human echocardiographer? Circulation 2018;138(16):1639–1642. DOI: 10.1161/CIRCULATIONAHA.118.037095
Omar AMS, Krittanawong C, Narula S, et al. Echocardiographic data in artificial intelligence research: primer on concepts of big data and latent states. JACC Cardiovasc Imaging 2020;13(1 Pt 1):170–172. DOI: 10.1016/j.jcmg.2019.07.017
Zhou J, Du M, Chang S, et al. Artificial intelligence in echocardiography: detection, functional evaluation, and disease diagnosis. Cardiovasc Ultrasound 2021;19(1):29. DOI: 10.1186/s12947-021-00261-2
Narang A, Bae R, Hong H, et al. Utility of deep learning algorithm to guide novices to acquire echocardiograms for limited diagnostic use. JAMA Cardiol 2021;6(6):624–632. DOI: 10.1001/jamacardio.2021.0185
Cheema B, Hsiesh C, Adams D, et al. Automated guidance and image capture of echocardiographic views using a deep learning derived technology. Circulation 2019;140 (Suppl 1): A15694-A.
Otto CM, Nishimura RA, Bonow RO, et al. 2020 ACC/AHA guideline for the management of patients with valvular heart disease: executive summary: a report of the american college of cardiology/american heart association joint committee on clinical practice guidelines. Circulation 2021;143(5):e35–e71. DOI: 10.1161/CIR.0000000000000932
Chandra V, Sarkar PG, Singh V. Mitral valve leaflet tracking in echocardiography using custom Yolo 3. Procedia Comput Sci 2020;171:820–828. DOI: 10.1016/j.procs.2020.04.089
Lang RM, Addetia K, Miyoshi T, et al. Use of machine learning to improve echocardiographic image interpretation workflow: a disruptive paradigm change? J Am Soc Echocardiogr 2021;34(4): 443–445. DOI: 10.1016/j.echo.2020.11.017
Andreassen BS, Veronesi F, Gerard O, et al. Mitral annular segmentation using deep learning in 3-D transesophageal echocardiography. IEEE J Biomed Health Inform 2020;24(4):994–1003. DOI: 10.1109/JBHI.2019.2959430
Kriltanwong C, Johnson KW, Baber U, et al. Deep learning for cardiovascular medicine: a practical primer. Eur Heart J 2019;40(25):2058–2073. DOI: 10.1093/eurheartj/ehz056
Kusunose K, Haga A, Abe T, et al. Utilization of artificial intelligence in echocardiography. Circ J 2019;83(8):1623–1629. DOI: 10.1253/circj.CJ-19-0420
Martins JFBS, Nascimento ER, Nascimento BR, et al. Towards automatic diagnosis of rheumatic heart disease on echocardiographic exams through video-based deep learning. J am Med Inform Assoc 2021;28(9):1834–1842. DOI: 10.1093/jamia/ocab061
Gosling AF, Thalappillil R, Ortoleva J, et al. Automated spectral Doppler profile tracing. J Cardiothorac Vasc Anaesth 2020;34(1):72–76. DOI: 10.1053/j.jvca.2019.06.018
Casadang–Verzosa G, Shertha S, Khalil MJ, et al. Network tomography for understanding phenotyping presentation in aortic stenosis. JACC Cardiovasc Imaging 2019;12(2):236–248. DOI: 10.1016/j.jcmg.2018.11.025
Sengupta PP, Shertha S, Kagiyam N, et al. A machine learning framework to identify distinct population with different phenotypes and outcomes. Circ Cardiovasc Imaging 2020;13:e009707.
Johri AM, Durbin J, Newbigging J, et al. Cardiac point of care ultrasound state-of-the-art in medical school education. J Am Soc Echocardiogr 2018;31(7):749–760. DOI: 10.1016/j.echo.2018.01.014
Wu H, Huynh TT, Souvenir R. Echocardiogram enhancement using supervised manifold denoising. Med Image Anal 2015;24(1):41–51. DOI: 10.1016/j.media.2015.05.004
Abdi AH, Luong C, Tsang T, et al. Automaticquality assessment of echocardiograms using convolutional neural networks: feasibility on the apical four-chamber view. IEEE Trans Med Imaging 2017;36(6):1221–1230. DOI: 10.1109/TMI.2017.2690836