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

Author:

Shad RohanORCID,Quach NicolasORCID,Fong Robyn,Kasinpila Patpilai,Bowles Cayley,Castro Miguel,Guha Ashrith,Suarez Erik E.,Jovinge StefanORCID,Lee Sangjin,Boeve Theodore,Amsallem Myriam,Tang Xiu,Haddad FrancoisORCID,Shudo Yasuhiro,Woo Y. JosephORCID,Teuteberg Jeffrey,Cunningham John P.ORCID,Langlotz Curtis P.ORCID,Hiesinger WilliamORCID

Abstract

AbstractDespite 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.

Funder

American Heart Association

Publisher

Springer Science and Business Media LLC

Subject

General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry

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