alvarouc/geisinger-echo-mortality

Demo of Geisinger's trained models applied to Stanford Ouyangs Dataset

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)

1. 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.

2. 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.

3. 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