River Jiang, Darwin F Yeung, Delaram Behnami, Christina Luong, Michael Y C Tsang, John Jue, Ken Gin, Parvathy Nair, Purang Abolmaesumi, Teresa S M Tsang
PMID: 35753590 DOI: 10.1016/j.echo.2022.06.005
Background: Unlike left ventricular (LV) ejection fraction, which provides a precise, reliable, and prognostically valuable measure of systolic function, there is no single analogous measure of LV diastolic function.
Objectives: We aimed to develop a continuous score to grade LV diastolic function using machine learning modeling of echocardiographic data.
Methods: Consecutive echo studies performed at a tertiary-care center between February 1, 2010, and March 31, 2016, were assessed, excluding studies containing features that would interfere with diastolic function assessment as well as studies in which 1 or more parameters within the contemporary diastolic function assessment algorithm were not reported. Diastolic function was graded based on 2016 American Society of Echocardiography (ASE)/European Association of Cardiovascular Imaging (EACVI) guidelines, excluding indeterminate studies. Machine learning models were trained (support vector machine [SVM], decision tree [DT], XGBoost [XGB], and dense neural network [DNN]) to classify studies within the training set by diastolic dysfunction severity, blinded to the ASE/EACVI classification. The DNN model was retrained to generate a regression model (R-DNN) to predict a continuous LV diastolic function score.
Results: A total of 28,986 studies were included; 23,188 studies were used to train the models, and 5,798 studies were used for validation. The models were able to reclassify studies with high agreement to the ASE/EACVI algorithm (SVM, 83%; DT, 100%; XGB, 100%; DNN, 98%). The continuous diastolic function score corresponded well with ASE/EACVI guidelines, with scores of 1.00 ± 0.01 for studies with normal function and 0.74 ± 0.05, 0.51 ± 0.06, and 0.27 ± 0.11 for mild, moderate, and severe diastolic dysfunction, respectively (mean ± 1 SD). A score of <0.91 predicted abnormal diastolic function (area under the receiver operator curve = 0.99), while a score of <0.65 predicted elevated filling pressure (area under the receiver operator curve = 0.99).
Conclusions: Machine learning can assimilate echocardiographic data and generate an automated continuous diastolic function score that corresponds well with current diastolic function grading recommendations.
Keywords: Artificial intelligence; Diastolic function; Echocardiography; Machine learning.
Follow instructions to install Docker on your system.
docker build -t diastology-predict .Use the input/test_input.csv as an example. This file can contain as many rows as you want. Save any number of these input files in the input/ directory.
lvef,LA_vol,tr_vel,E,Lat_E,Septal_E,EAratio,avgEeratio,myocardial_dz
65.0,32.1428571428571,1.8027756377319943,63.0,5.1,3.7,0.68,14.32,False
docker run -v $(pwd)/input:/app/input -v $(pwd)/output:/app/output diastology-predictAll output files will be saved into the output/ directory for further analysis.
Alternatively, run the following command to save the standard output and standard error streams from the Docker image for debugging purposes.
docker run -v $(pwd)/input:/app/input -v $(pwd)/output:/app/output diastology-predict /bin/bash -c "python predict.py > /app/output/output.log 2>&1"