Echocardiography, or cardiac ultrasound, is the most widely used and readily available imaging modality to assess cardiac function and structure. However, human assessment of cardiac function focuses on a limited sampling of cardiac cycles and has considerable inter-observer variability despite years of training. EchoNet-Dynamic - a video-based deep learning algorithm that surpasses the performance of human experts in the critical tasks of segmenting the left ventricle, estimating ejection fraction and assessing cardiomyopathy. Accurate assessment of cardiac function is crucial for the diagnosis of cardiovascular disease, screening for cardiotoxicity and decisions regarding the clinical management of patients with a critical illness. By leveraging information across multiple cardiac cycles, the 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. In an external dataset from another healthcare system, EchoNet-Dynamic predicts the ejection fraction and classifies heart failure with reduced ejection fraction. Prospective evaluation with repeated human measurements confirms that the model has variance that is comparable to or less than that of human experts.
Input variables : Cardiac Ultrasound
Output Variables : Heart Segmentation
| Business | : | Population at High Risk of Disease | Risk by Geography | Risk by Demographics | Risk by Clinical Parameters | Optimized Hospital Resource Utilization | Decreased Cost of Care | Decreased Patient Visits |
| Infrastructure | : | Log Bytes | Logging/User/IAMPolicy | Logging/User/VPN | CPU Utilization | Memory Usage | Error Count | Prediction Count | Prediction Latencies | Private Endpoint Prediction Latencies | Private Endpoint Response Count |
Visit Model : nature.com
Additional links : github.com
| Model Category | : | Public |
| Date Published | : | March, 2020 |
| Healthcare Domain | : | Provider |
| Code | : | github.com |
| Medical Imaging |
| Health Risk Management |
| Health Risk Prediction |