Echo mortality model applied to Stanford Dataset
Demo of Geisinger's trained models applied to Stanford Dataset [1]
[1] Video-based AI for beat-to-beat assessment of cardiac function. David Ouyang, Bryan He, Amirata Ghorbani, Neal Yuan, Joseph Ebinger, Curt P. Langlotz, Paul A. Heidenreich, Robert A. Harrington, David H. Liang, Euan A. Ashley, and James Y. Zou. Nature (2020)
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Obtain Stanford Dataset
Register at: https://echonet.github.io/dynamic/index.html#dataset
Once given access, download and unzip the file EchoNet-Dynamic.zip
In this demo, we unzipped the file to the folder path : /data/EchoData/aeulloacerna/EchoNet-Dynamic/
Update the path to the data in the Makefile by modifying the variable
STANFORD_DATA
accordingly. -
Building container
At the root of the repository type
make build
This will prepare a Docker image named geisinger-echo-mortality:v1. This image is used in the next steps to load and apply the models to the Stanford dataset.
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Apply the AP4 model to Stanford Dataset
At the root of the repository type
make run
This will open a docker container where the AP4 model will run
Two files will be generated:
- FileList_predicted.csv
- stanford_risk_vs_ef.pdf