Abstract
Accurate assessment of cardiac function is crucial for diagnosing cardiovascular disease1, screening for cardiotoxicity2,3, and deciding clinical management in patients with critical illness4. However human assessment of cardiac function focuses on a limited sampling of cardiac cycles and has significant interobserver variability despite years of training2,5,6. To overcome this challenge, we present the first beat-to-beat deep learning algorithm that surpasses human expert performance in the critical tasks of segmenting the left ventricle, estimating ejection fraction, and assessing cardiomyopathy. Trained on echocardiogram videos, our model accurately segments the left ventricle with a Dice Similarity Coefficient of 0.92, predicts ejection fraction with mean absolute error of 4.1%, and reliably classifies heart failure with reduced ejection fraction (AUC of 0.97). Prospective evaluation with repeated human measurements confirms that our model has less variance than experts. By leveraging information across multiple cardiac cycles, our model can identify subtle changes in ejection fraction, is more reproducible than human evaluation, and lays the foundation for precise diagnosis of cardiovascular disease. As a new resource to promote further innovation, we also make publicly available one of the largest medical video dataset of over 10,000 annotated echocardiograms.
Key Points
Video based deep learning evaluation of cardiac ultrasound accurately identifies cardiomyopathy and predict ejection fraction, the most common metric of cardiac function.
Using human tracings obtained during standard clinical workflow, deep learning semantic segmentation accurately labels the left ventricle frame-by-frame, including in frames without prior human annotation.
Computational cardiac function analysis allows for beat-by-beat assessment of ejection fraction, which more accurately assesses cardiac function in patients with atrial fibrillation, arrhythmias, and heart rate variability.
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
This work is supported by the Stanford Translational Research and Applied Medicine pilot grant, Stanford Cardiovascular Institute pilot grant, and a Stanford Artificial Intelligence in Imaging and Medicine Center seed grant. D.O. is supported by the American College of Cardiology Foundation / Merck Research Fellowship. B.H. is supported by the NSF Graduate Research Fellowship. A.G. is supported by the Stanford-Robert Bosch Graduate Fellowship in Science and Engineering. J.Z. is supported NSF CCF 1763191, NIH R21 MD012867-01, NIH P30AG059307 and by a Chan-Zuckerberg Biohub Fellowship.
Author Declarations
All relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript.
Yes
All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.
Yes
I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).
Yes
I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.
Yes
Data Availability
The data is publicly available with a noncommerical data use agreement.