Video-based AI for beat-to-beat assessment of cardiac function
By David Ouyang, Bryan He, Amirata Ghorbani, N. Yuan, J. Ebinger, Curtis P. Langlotz, P. Heidenreich, Robert A. Harrington, David H. Liang, Euan A. Ashley and 1 more. Nature, 2020
1,040 citations recorded by the source corpus.
Accurate assessment of cardiac function is crucial for the diagnosis of cardiovascular disease1, screening for cardiotoxicity2 and decisions regarding the clinical management of patients with a critical illness3. However, human assessment of cardiac function focuses on a limited sampling of cardiac cycles and has considerable inter-observer variability despite years of training4,5. Here, to overcome this challenge, we present a video-based deep learning algorithm—EchoNet-Dynamic—that surpasses the performance of human experts 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 a mean absolute error of 4.1% and reliably classifies heart failure with reduced ejection fraction (area under the curve of 0.97). In an external dataset from another healthcare system, EchoNet-Dynamic predicts the ejection fraction with a mean absolute error of 6.0% and classifies heart failure with reduced ejection fraction with an area under the curve of 0.96. Prospective evaluation with repeated human measurements confirms that the model has variance that is comparable to or less than that of human experts. By leveraging information across multiple cardiac cycles, our model can rapidly identify subtle changes in ejection fraction, is more reproducible than human evaluation and lays the foundation for precise diagnosis of cardiovascular disease in real time. As a resource to promote further innovation, we also make publicly available a large dataset of 10,030 annotated echocardiogram videos. A video-based deep learning algorithm—EchoNet-Dynamic—accurately identifies subtle changes in ejection fraction and classifies heart failure with reduced ejection fraction using information from multiple cardiac cycles.
Subfields: Cardiology and Cardiovascular Medicine, Emergency Medicine, Surgery
View Paper: https://www.semanticscholar.org/paper/6f472cbad2e9aa1d2f365d3a95c2a2f946d11b31
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Citations in the corpus
- Shifting machine learning for healthcare from development to deployment and from models to data (2022, background): “In the past decade, many deep-learning models for medical-image classification 3,37 , segmentation 38,39 and detection 40 have achieved physician-level performance.”
- AI in health and medicine (2022, background): “AI systems have achieved considerable improvements in accuracy for radiology tasks, including mammography interpretation 21,22 , cardiac function assessment 23,24 and lung cancer screening 25 , tackling not only diagnosis but also risk prediction and treatment 26 .”
- Segment anything in medical images (2023, neutral): “Accurate segmentation is essential for many clinical applications, including disease diagnosis, treatment planning, and monitoring of disease progression [1], [2].”