Contemporary applications of artificial intelligence and machine learning in echocardiography
Lancellotti, P. et al. The use of echocardiography in acute cardiovascular care: recommendations of the European Association of Cardiovascular Imaging and the Acute Cardiovascular Care Association. Eur. Heart J. Cardiovasc. Imaging 16, 119–146 (2015).
Google Scholar
Bunting, K. V. et al. A practical guide to assess the reproducibility of echocardiographic measurements. J. Am. Soc. Echocardiogr. 32, 1505–1515 (2019).
Google Scholar
Kusunose, K. et al. Clinically feasible and accurate view classification of echocardiographic images using deep learning. Biomolecules 10, 665 (2020).
Google Scholar
Madani, A., Arnaout, R., Mofrad, M. & Arnaout, R. Fast and accurate view classification of echocardiograms using deep learning. NPJ Digit. Med. 1, 6 (2018).
Google Scholar
Jentzer, J. C., Kashou, A. H. & Murphree, D. H. Clinical applications of artificial intelligence and machine learning in the modern cardiac intensive care unit. Intell. Based Med. 7, 100089 (2023).
Google Scholar
Pinto-Coelho, L. How artificial intelligence is shaping medical imaging technology: a survey of innovations and applications. Bioengineering10, 1435 (2023).
Google Scholar
Hughes, J. W. et al. Deep learning evaluation of biomarkers from echocardiogram videos. EBioMedicine 73, 103613 (2021).
Google Scholar
Sarker, I. H. Machine learning: algorithms, real-world applications and research directions. SN Comput. Sci. 2, 160 (2021).
Google Scholar
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
Google Scholar
Berg, E. A. R. et al. Fully automatic estimation of global left ventricular systolic function using deep learning in transoesophageal echocardiography. Eur. Heart J. Imaging Methods Pract. 1, qyad007 (2023).
Google Scholar
Sfakianakis, C., Simantiris, G. & Tziritas, G. GUDU: Geometrically-constrained Ultrasound Data augmentation in U-Net for echocardiography semantic segmentation. Biomed. Signal. Process. Control 82, 104557 (2023).
Google Scholar
Ronneberger, O., Fischer, P. & Brox, T. U-Net: convolutional networks for biomedical image segmentation. in Lecture Notes in Computer Science 234–241 (Springer International Publishing, 2015).
Selvaraju, R. R. et al. Grad-CAM: Visual explanations from deep networks via gradient-based localization. Int. J. Comput. Vis. 128, 336–359 (2020).
Google Scholar
Kosaraju, A., Goyal, A., Grigorova, Y. & Makaryus, A. N. Left Ventricular Ejection Fraction. (StatPearls Publishing, 2023).
Knackstedt, C. et al. Fully automated versus standard tracking of left ventricular ejection fraction and longitudinal strain: the FAST-EFs multicenter study. J. Am. Coll. Cardiol. 66, 1456–1466 (2015).
Google Scholar
Naser, J. A. et al. Artificial intelligence-based classification of echocardiographic views. Eur Heart J. Digit. Health 5, 260–269 (2024).
Google Scholar
Steffner, K. R. et al. Deep learning for transesophageal echocardiography view classification. Sci. Rep. 14, 11 (2024).
Google Scholar
Chen, C. et al. Deep learning for cardiac image segmentation: a review. Front. Cardiovasc. Med. 7, 25 (2020).
Google Scholar
Liu, X. et al. Deep learning-based automated left ventricular ejection fraction assessment using 2-D echocardiography. Am. J. Physiol. Heart Circ. Physiol. 321, H390–H399 (2021).
Google Scholar
Ouyang, D. et al. Video-based AI for beat-to-beat assessment of cardiac function. Nature 580, 252–256 (2020).
Google Scholar
Ghorbani, A. et al. Deep learning interpretation of echocardiograms. NPJ Digit. Med. 3, 10 (2020).
Google Scholar
Reddy, C. D., Lopez, L., Ouyang, D., Zou, J. Y. & He, B. Video-based deep learning for automated assessment of left ventricular ejection fraction in pediatric patients. J. Am. Soc. Echocardiogr. 36, 482–489 (2023).
Google Scholar
Yamaguchi, N., Kosaka, Y., Haga, A., Sata, M. & Kusunose, K. Artificial intelligence-assisted interpretation of systolic function by echocardiogram. Open Heart 10, e002287 (2023).
Google Scholar
Olaisen, S. et al. Automatic measurements of left ventricular volumes and ejection fraction by artificial intelligence: clinical validation in real time and large databases. Eur. Heart J. Cardiovasc. Imaging 25, 383–395 (2024).
Google Scholar
He, B. et al. AI-Enabled assessment of cardiac function and video quality in emergency department point-of-care echocardiograms. J. Emerg. Med. 66, 184–191 (2024).
Google Scholar
Asch, F. M. et al. Deep learning-based automated echocardiographic quantification of left ventricular ejection fraction: a point-of-care solution. Circ. Cardiovasc. Imaging 14, e012293 (2021).
Google Scholar
Gohar, E. et al. Artificial Intelligence (AI) versus POCUS expert: a validation study of three automatic AI-Based, real-time, hemodynamic echocardiographic assessment tools. J. Clin. Med. Res. 12, 1352 (2023).
Luong, C. L. et al. Validation of machine learning models for estimation of left ventricular ejection fraction on point-of-care ultrasound: insights on features that impact performance. Echo Res. Pract. 11, 9 (2024).
Google Scholar
2023 ACC Expert Consensus on Management of HFpEF: Key Points. American College of Cardiology https://www.acc.org/Latest-in-Cardiology/ten-points-to-remember/2023/04/17/16/40/2023-acc-expert-consensus-on-hfpef.
Tsao, C. W. et al. Heart disease and stroke statistics-2022 update: a report from the American Heart Association. Circulation 145, e153–e639 (2022).
Google Scholar
Ho, J. E., Redfield, M. M., Lewis, G. D., Paulus, W. J. & Lam, C. S. P. Deliberating the diagnostic dilemma of heart failure with preserved ejection fraction. Circulation 142, 1770–1780 (2020).
Google Scholar
Redfield, M. M. & Borlaug, B. A. Heart failure with preserved ejection fraction: a review. JAMA 329, 827–838 (2023).
Google Scholar
Chen, X. et al. Artificial intelligence-assisted left ventricular diastolic function assessment and grading: multiview versus single view. J. Am. Soc. Echocardiogr. 36, 1064–1078 (2023).
Google Scholar
Chiou, Y.-A., Hung, C.-L. & Lin, S.-F. AI-Assisted echocardiographic prescreening of heart failure with preserved ejection fraction on the basis of intrabeat dynamics. JACC Cardiovasc. Imaging 14, 2091–2104 (2021).
Google Scholar
Akerman, A. P. et al. Automated echocardiographic detection of heart failure with preserved ejection fraction using artificial intelligence. JACC: Adv. 2, 100452 (2023).
Google Scholar
Kobayashi, M. et al. Machine learning-derived echocardiographic phenotypes predict heart failure incidence in asymptomatic individuals. JACC Cardiovasc. Imaging 15, 193–208 (2022).
Google Scholar
Carluccio, E. et al. Left atrial strain in the assessment of diastolic function in heart failure: a machine learning approach. Circ. Cardiovasc. Imaging 16, e014605 (2023).
Google Scholar
Gruca, M. M. et al. Noninvasive assessment of left ventricular end-diastolic pressure using machine learning-derived phasic left atrial strain. Eur. Heart J. Cardiovasc. Imaging 25, 18–26 (2023).
Google Scholar
Xu, B. et al. Artificial intelligence machine learning based evaluation of elevated left ventricular end-diastolic pressure: a cleveland Clinic cohort study. Cardiovasc. Diagn. Ther. 14, 788–797 (2024).
Google Scholar
Fang, M. Z. et al. Abstract 12374: machine learning-based evaluation of elevated left ventricular end-diastolic pressure. Circulation 148, A12374–A12374 (2023).
Konstam, M. A. et al. Evaluation and management of right-sided heart failure: a scientific statement from the American Heart Association. Circulation 137, e578–e622 (2018).
Google Scholar
Tokodi, M. et al. Deep learning-based prediction of right ventricular ejection fraction using 2D echocardiograms. JACC Cardiovasc. Imaging 16, 1005–1018 (2023).
Google Scholar
Kampaktsis, P. N. et al. An attention-based deep learning method for right ventricular quantification using 2D echocardiography: Feasibility and accuracy. Echocardiography 41, e15719 (2024).
Google Scholar
Shad, R. et al. Predicting post-operative right ventricular failure using video-based deep learning. Nat. Commun. 12, 5192 (2021).
Google Scholar
Liu, S. et al. Artificial intelligence-based assessment of indices of right ventricular function. J. Cardiothorac. Vasc. Anesth. 34, 2698–2702 (2020).
Google Scholar
Holste, G. et al. Severe aortic stenosis detection by deep learning applied to echocardiography. Eur. Heart J. 44, 4592–4604 (2023).
Google Scholar
Dai, W., Nazzari, H., Namasivayam, M., Hung, J. & Stultz, C. M. Identifying aortic stenosis with a single parasternal long-axis video using deep learning. J. Am. Soc. Echocardiogr. 36, 116–118 (2023).
Google Scholar
Thalappillil, R. et al. Artificial intelligence for the measurement of the aortic valve annulus. J. Cardiothorac. Vasc. Anesth. 34, 65–71 (2020).
Google Scholar
Krishna, H. et al. Fully automated artificial intelligence assessment of aortic stenosis by echocardiography. J. Am. Soc. Echocardiogr. 36, 769–777 (2023).
Google Scholar
Wessler, B. S. et al. Automated detection of aortic stenosis using machine learning. J. Am. Soc. Echocardiogr. 36, 411–420 (2023).
Google Scholar
Lachmann, M. et al. Subphenotyping of patients with aortic stenosis by unsupervised agglomerative clustering of echocardiographic and hemodynamic data. JACC Cardiovasc. Interv. 14, 2127–2140 (2021).
Google Scholar
Sánchez-Puente, A. et al. Machine learning to optimize the echocardiographic follow-up of aortic stenosis. JACC Cardiovasc. Imaging 16, 733–744 (2023).
Google Scholar
Strange, G., Stewart, S., Watts, A. & Playford, D. Enhanced detection of severe aortic stenosis via artificial intelligence: a clinical cohort study. Open Heart 10, e002265 (2023).
Google Scholar
Hausleiter, J. et al. Artificial intelligence-derived risk score for mortality in secondary mitral regurgitation treated by transcatheter edge-to-edge repair: the EuroSMR risk score. Eur. Heart J. 45, 922–936 (2024).
Google Scholar
Sadeghpour, A. et al. An automated machine learning-based quantitative multiparametric approach for mitral regurgitation severity grading. JACC Cardiovasc. Imaging (2024).
Google Scholar
Bernard, J. et al. Integrating echocardiography parameters with explainable artificial intelligence for data-driven clustering of primary mitral regurgitation phenotypes. JACC Cardiovasc. Imaging 16, 1253–1267 (2023).
Google Scholar
Yang, F. et al. Automated analysis of doppler echocardiographic videos as a screening tool for valvular heart diseases. JACC Cardiovasc. Imaging 15, 551–563 (2022).
Google Scholar
Xu, B. & Sanchez-Nadales, A. Artificial intelligence in echocardiographic evaluation of mitral regurgitation: envisioning the future. JACC Cardiovasc. Imaging (2024).
Google Scholar
Andreassen, B. S., Veronesi, F., Gerard, O., Solberg, A. H. S. & Samset, E. Mitral annulus segmentation using deep learning in 3-D transesophageal echocardiography. IEEE J. Biomed. Health Inform. 24, 994–1003 (2020).
Google Scholar
Edwards, L. A. et al. Machine learning for pediatric echocardiographic mitral regurgitation detection. J. Am. Soc. Echocardiogr. 36, 96–104.e4 (2023).
Google Scholar
Brown, K. et al. Using artificial intelligence for rheumatic heart disease detection by echocardiography: focus on mitral regurgitation. J. Am. Heart Assoc. 13, e031257 (2024).
Google Scholar
Martins, J. F. B. S. et al. Towards automatic diagnosis of rheumatic heart disease on echocardiographic exams through video-based deep learning. J. Am. Med. Inform. Assoc. 28, 1834–1842 (2021).
Google Scholar
Peck, D. et al. The use of Artificial Intelligence Guidance for rheumatic heart disease screening by novices. J. Am. Soc. Echocardiogr. 36, 724–732 (2023).
Google Scholar
Trenkwalder, T. et al. Machine learning identifies pathophysiologically and prognostically informative phenotypes among patients with mitral regurgitation undergoing transcatheter edge-to-edge repair. Eur. Heart J. Cardiovasc. Imaging 24, 574–587 (2023).
Google Scholar
Liu, B. et al. A deep learning framework assisted echocardiography with diagnosis, lesion localization, phenogrouping heterogeneous disease, and anomaly detection. Sci. Rep. 13, 3 (2023).
Google Scholar
Duffy, G. et al. High-throughput precision phenotyping of left ventricular hypertrophy with cardiovascular deep learning. JAMA Cardiol. 7, 386–395 (2022).
Google Scholar
Goto, S. et al. Artificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms. Nat. Commun. 12, 2726 (2021).
Google Scholar
Hussain, K. & Shetty, M. Cardiac sarcoidosis. in StatPearls (StatPearls Publishing, 2024).
Katsushika, S. et al. Deep learning algorithm to detect cardiac sarcoidosis from echocardiographic movies. Circ. J. 86, 87–95 (2021).
Google Scholar
Eche, T., Schwartz, L. H., Mokrane, F.-Z. & Dercle, L. Toward generalizability in the deployment of artificial intelligence in radiology: role of computation stress testing to overcome underspecification. Radiol. Artif. Intell. 3, e210097 (2021).
Google Scholar
Porter, T. R. et al. Guidelines for the use of echocardiography as a monitor for therapeutic intervention in adults: a report from the American Society of Echocardiography. J. Am. Soc. Echocardiogr. 28, 40–56 (2015).
Google Scholar
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