Video-based AI for beat-to-beat assessment of cardiac function

  • 1.

    Ziaeian, B. & Fonarow, G. C. Epidemiology and aetiology of heart failure. Nat. Rev. Cardiol. 13, 368–378 (2016).

  • 2.

    Shakir, D. K. & Rasul, K. I. Chemotherapy induced cardiomyopathy: pathogenesis, monitoring and management. J. Clin. Med. Res. 1, 8–12 (2009).

  • 3.

    Dellinger, R. P. et al. Surviving Sepsis Campaign: international guidelines for management of severe sepsis and septic shock, 2012. Intensive Care Med. 39, 165–228 (2013).

  • 4.

    Farsalinos, K. E. et al. Head-to-head comparison of global longitudinal strain measurements among nine different vendors: the EACVI/ASE Inter-Vendor Comparison Study. J. Am. Soc. Echocardiogr. 28, 1171–1181 (2015).

  • 5.

    Lang, R. M. et al. Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. Eur. Heart J. Cardiovasc. Imaging 16, 233–271 (2015).

  • 6.

    McMurray, J. J. et al. ESC guidelines for the diagnosis and treatment of acute and chronic heart failure 2012. Eur. J. Heart Fail. 14, 803–869 (2012).

  • 7.

    Loehr, L. R., Rosamond, W. D., Chang, P. P., Folsom, A. R. & Chambless, L. E. Heart failure incidence and survival (from the Atherosclerosis Risk in Communities study). Am. J. Cardiol. 101, 1016–1022 (2008).

  • 8.

    Bui, A. L., Horwich, T. B. & Fonarow, G. C. Epidemiology and risk profile of heart failure. Nat. Rev. Cardiol. 8, 30–41 (2011).

  • 9.

    Roizen, M. F. Forecasting the future of cardiovascular disease in the United States: a policy statement from the American Heart Association. Yearbook Anesthesiol. Pain Manage. 2012, 12–13 (2012).

  • 10.

    Yancy, C. W. et al. 2013 ACCF/AHA guideline for the management of heart failure. Circulation 128, e240–e327 (2013).

  • 11.

    Huang, H. et al. Accuracy of left ventricular ejection fraction by contemporary multiple gated acquisition scanning in patients with cancer: comparison with cardiovascular magnetic resonance. J. Cardiovasc. Magn. Reson. 19, 34 (2017).

  • 12.

    Pellikka, P. A. et al. Variability 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. JAMA Netw. Open 1, e181456 (2018).

  • 13.

    Malm, S., Frigstad, S., Sagberg, E., Larsson, H. & Skjaerpe, T. Accurate and reproducible measurement of left ventricular volume and ejection fraction by contrast echocardiography: a comparison with magnetic resonance imaging. J. Am. Coll. Cardiol. 44, 1030–1035 (2004).

  • 14.

    Cole, G. D. et al. Defining the real-world reproducibility of visual grading of left ventricular function and visual estimation of left ventricular ejection fraction: impact of image quality, experience and accreditation. Int. J. Cardiovasc. Imaging 31, 1303–1314 (2015).

  • 15.

    Koh, A. S. et al. A comprehensive population-based characterization of heart failure with mid-range ejection fraction. Eur. J. Heart Fail. 19, 1624–1634 (2017).

  • 16.

    Chioncel, O. et al. Epidemiology and one-year outcomes in patients with chronic heart failure and preserved, mid-range and reduced ejection fraction: an analysis of the ESC Heart Failure Long-Term Registry. Eur. J. Heart Fail. 19, 1574–1585 (2017).

  • 17.

    Shah, K. S. et al. Heart failure with preserved, borderline, and reduced ejection fraction: 5-year outcomes. J. Am. Coll. Cardiol. 70, 2476–2486 (2017).

  • 18.

    Papolos, A., Narula, J., Bavishi, C., Chaudhry, F. A. & Sengupta, P. P. U.S. hospital use of echocardiography: insights from the nationwide inpatient sample. J. Am. Coll. Cardiol. 67, 502–511 (2016).

  • 19.

    Douglas, P. S. et al. ACCF/ASE/AHA/ASNC/HFSA/HRS/SCAI/SCCM/SCCT/SCMR 2011 Appropriate use criteria for echocardiography. J. Am. Soc. Echocardiogr. 24, 229–267 (2011).

  • 20.

    Zhang, J. et al. Fully automated echocardiogram interpretation in clinical practice: feasibility and diagnostic accuracy. Circulation 138, 1623–1635 (2018).

  • 21.

    Madani, A., Arnaout, R., Mofrad, M. & Arnaout, R. Fast and accurate view classification of echocardiograms using deep learning. NPJ Digit. Med. 1, 6 (2018).

  • 22.

    Ghorbani, A. et al. Deep learning interpretation of echocardiograms. NPJ Digit. Med. 3, 10 (2020).

  • 23.

    Behnami, D. et al. in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support 65–73 (Springer, 2018).

  • 24.

    Ardila, D. et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat. Med. 25, 954–961 (2019).

  • 25.

    Poplin, R. et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat. Biomed. Eng. 2, 158–164 (2018).

  • 26.

    Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017).

  • 27.

    Coudray, N. et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat. Med. 24, 1559–1567 (2018).

  • 28.

    Chen, L.-C., Papandreou, G., Schroff, F. & Adam, H. Rethinking atrous convolution for semantic image segmentation. Preprint at https://arxiv.org/abs/1706.05587 (2017).

  • 29.

    Tran, D. et al. A closer look at spatiotemporal convolutions for action recognition. In Proc. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 6450–6459 (2018).

  • 30.

    Tran, D., Bourdev, L., Fergus, R., Torresani, L. & Paluri, M. Learning spatiotemporal features with 3D convolutional networks. In Proc. IEEE International Conference on Computer Vision 4489–4497 (2015).

  • 31.

    Kay, W. et al. The kinetics human action video dataset.Preprint at https://arxiv.org/abs/1705.06950 (2017).