Predicting post-operative right ventricular failure using video-based deep learning

Rohan Shad, Nicolas Quach, Robyn Fong, Patpilai Kasinpila, Cayley Bowles, Miguel Castro, Ashrith Guha, Erik E. Suarez, Stefan Jovinge, Sangjin Lee, Theodore Boeve, Myriam Amsallem, Xiu Tang, Francois Haddad, Yasuhiro Shudo, Y. Joseph Woo, Jeffrey Teuteberg, John P. Cunningham, Curtis P. Langlotz and William Hiesinger ()
Additional contact information
Rohan Shad: Stanford University
Nicolas Quach: Stanford University
Robyn Fong: Stanford University
Patpilai Kasinpila: Stanford University
Cayley Bowles: Stanford University
Miguel Castro: Houston Methodist DeBakey Heart Centre
Ashrith Guha: Houston Methodist DeBakey Heart Centre
Erik E. Suarez: Houston Methodist DeBakey Heart Centre
Stefan Jovinge: Spectrum Health Grand Rapids
Sangjin Lee: Spectrum Health Grand Rapids
Theodore Boeve: Spectrum Health Grand Rapids
Myriam Amsallem: Stanford University
Xiu Tang: Stanford University
Francois Haddad: Stanford University
Yasuhiro Shudo: Stanford University
Y. Joseph Woo: Stanford University
Jeffrey Teuteberg: Stanford University
John P. Cunningham: Columbia University
Curtis P. Langlotz: Stanford Artificial Intelligence in Medicine Centre
William Hiesinger: Stanford University

Nature Communications, 2021, vol. 12, issue 1, 1-8

Abstract: Abstract Despite progressive improvements over the decades, the rich temporally resolved data in an echocardiogram remain underutilized. Human assessments reduce the complex patterns of cardiac wall motion, to a small list of measurements of heart function. All modern echocardiography artificial intelligence (AI) systems are similarly limited by design – automating measurements of the same reductionist metrics rather than utilizing the embedded wealth of data. This underutilization is most evident where clinical decision making is guided by subjective assessments of disease acuity. Predicting the likelihood of developing post-operative right ventricular failure (RV failure) in the setting of mechanical circulatory support is one such example. Here we describe a video AI system trained to predict post-operative RV failure using the full spatiotemporal density of information in pre-operative echocardiography. We achieve an AUC of 0.729, and show that this ML system significantly outperforms a team of human experts at the same task on independent evaluation.

Date: 2021
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DOI: 10.1038/s41467-021-25503-9

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