Ouyang, D. et al. Video-based AI for beat-by-beat assessment of cardiac function. Nature 580252–256 (2020).
Duffy, G. et al. High-throughput precision phenotyping of left ventricular hypertrophy by deep cardiovascular learning. jam 7386–395 (2022).
Google Scholar
Zhang, J. et al. Fully automated echocardiogram interpretation in clinical practice. Circulation 1381623–1635 (2018).
Heidenreich, Pennsylvania et al. 2022 AHA/ACC/HFSA Heart Failure Management Guidelines: Report of the American College of Cardiology/American Heart Association Clinical Practice Guidelines Joint Committee. Circulation 145e895–e1032 (2022).
Dunlay, SM, Roger, VL & Redfield, MM Epidemiology of heart failure with preserved ejection fraction. nut. Rev. Cardiol. 14591–602 (2017).
Al-Khatib, SM et al. 2017 AHA/ACC/HRS Guidelines for Management of Patients with Ventricular Arrhythmias and Prevention of Sudden Cardiac Death: American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and Arrhythmia Society Report. Circulation 138e272–e391 (2018).
Wilcox, JE, Fang, JC, Margulies, KB & Mann, DL Heart failure with recovery of left ventricular ejection fraction: JACC Scientific Expert Panel. crowded. Col. cardiol. 76719–734 (2020).
Yuan, N. et al. Systematic quantification of sources of variation in ejection fraction calculations using deep learning. JACC Cardiovascular.imaging 142260–2262 (2021).
Pellikka, Pennsylvania et al. Variations in ejection fraction measured by echocardiography, gated single-photon emission computed tomography, and cardiac magnetic resonance in patients with coronary artery disease and left ventricular dysfunction. Open JAMA net 1e181456 (2018).
Lang, RM et al. Recommendations for cardiac chamber quantification by echocardiography in adults: Updates from the American Society of Echocardiography and the European Association for Cardiovascular Imaging. EUR. Heart J. Cardiovasc.imaging 16233–270 (2015).
Cole, GD etc. Defining real-world reproducibility of visual grading of left ventricular function and visual estimation of left ventricular ejection fraction: the impact of image quality, experience, and certification. Inside and outside J. Cardiovasc.imaging 311303–1314 (2015).
Shahgaldi, K., Gudmundsson, P., Manouras, A., Brodin, L.-A. & Winter, R. Visually estimated ejection fraction by two-dimensional and three-plane echocardiography is a real-time three-dimensional It correlates closely with the quantitative ejection fraction by echocardiography. cardiovascular.ultrasound 741 (2009).
Stone, JR, Kanneganti, R., Abbasi, M. & Akhtari, M. Monitoring chemotherapy-related cardiotoxicity in the form of left ventricular systolic dysfunction: a review of current recommendations. JCO Oncol. Practice. 17228–236 (2021).
Chen, JH & Asch, SM Machine Learning and Prediction in Medicine – Beyond the Peak of Inflated Expectations. N. Engl. J. Med. 3762507–2509 (2017).
Huang, M.-S., Wang, C.-S., Chiang, J.-H., Liu, P.-Y. & Tsai, W.-C. Deep neural network interpretation of transthoracic echocardiography Automatic recognition of local wall motion anomalies. Circulation 1421510–1520 (2020).
Wu, E. et al. How medical AI devices are evaluated: limitations and recommendations from an FDA-approved analysis. nut. medicine. 27582–584 (2021).
Schiano, C. et al. Integrating machine learning and bioinformatics frameworks into the discovery of potential familial DCM-associated markers. gene 121946 (2021).
Infante, T. et al. Radiogenomics and artificial intelligence approaches applied to cardiac computed tomography angiography and cardiac magnetic resonance for precision medicine in coronary heart disease: a systematic review. Circumferential cardiovascular.imaging 141133–1146 (2021).
Bazoukis, G. et al. Embedding Augmented Intelligence in Medicine: A Framework for Successful Implementation. Cell Rep. Med. 3100485 (2022).
Bang, H., Ni, L. & Davis, CE Evaluation of blinding in clinical trials. Control. Clin.ordeal twenty five143–156 (2004).
Yao, X. et al. An artificial intelligence-enabled electrocardiogram for identifying patients with low ejection fraction: a pragmatic randomized clinical trial. nut. medicine. 27815–819 (2021).
Attia, ZI et al. An Artificial Intelligence-Enabled ECG Algorithm for Identifying Atrial Fibrillation Patients in Sinus Rhythm: A Retrospective Analysis of Outcome Prediction. lancet 394861–867 (2019).
Noseworthy, PA, etc. Artificial intelligence-guided screening of atrial fibrillation using electrocardiography during sinus rhythm: a prospective nonrandomized interventional trial. lancet 4001206–1212 (2022).
Farsalinos, KE et al. Direct Comparison of Global Longitudinal Strain Measurements Across Nine Different Vendors: An EACVI/ASE Cross-Vendor Study. crowded.social echocardiogram 281171–1181.e2 (2015).
Purcell, SD etc. Effects of home blood pressure monitoring via a smartphone hypertension coaching or tracking application for adults with uncontrolled hypertension: a randomized clinical trial. Open JAMA net 3e200255 (2020).
Vodrahalli, K., Daneshjou, R., Gerstenberg, T. & Zou, J. Do humans trust more advice from AI? Minutes 2022 AAAI/ACM Conference on AI, Ethics and Society 763–777 (Computing Machinery Society, 2022).
Liu, X. et al. Reporting guidelines for clinical trial reports of interventions involving artificial intelligence: the CONSORT-AI extension. nut. medicine. 261364–1374 (2020).
Cheitlin, MD et al. ACC/AHA guidelines for the clinical application of echocardiography. Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Committee on Clinical Applications of Echocardiography). Developed in collaboration with the American Echocardiography Society. Circulation 951686–1744 (1997).
Douglas, PS et al. Echocardiographic Imaging in Clinical Trials: American Society of Echocardiography Core Laboratory Standards: Approved by the American College of Cardiology. crowded.social echocardiogram twenty two755-765 (2009).
Douglas, PS et al. 2019 ACC/AHA/ASE Key Data Elements and Transthoracic Echocardiography Definitions: American College of Cardiology/American Heart Association Task Force on Clinical Data Criteria (Committee to Develop Clinical Data Criteria for Transthoracic Echocardiography) ) and the report of the American Society of Echocardiography. Circumferential cardiovascular.imaging 12e000027 (2019).
CONSORT-AI and SPIRIT-AI steering groups. We need reporting guidelines for clinical trials evaluating artificial intelligence interventions. nut. medicine. twenty five1467–1468 (2019).
