Noninvasive estimation of aortic hemodynamics and cardiac contractility using machine learning

Figures

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  Figure 1. Schematic illustration of the regression pipeline. brSBP Brachial systolic blood pressure, brDBP brachial diastolic blood pressure, HR heart rate, cfPWV carotid-to-femoral pulse wave velocity, and EF ejection fraction were used as features for predicting aortic systolic blood pressure (aSBP), cardiac output (CO), and end-systolic elastance (Ees). Regression models were trained to map the input data to the respective target data of interest. The methodology presented here was followed for each regression process (in terms of set of inputs, model, and output).

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  Figure 2. Comparison between predicted and reference data. Scatterplots and Bland–Altman plots between: (A, B) the predicted aSBP and the reference aSBP, and (C, D) the predicted CO and the reference CO. The solid line of the scatterplots represents equality. In Bland–Altman plots, limits of agreement (LoA), within which 95% of errors are expected to lie, are defined by the two horizontal dashed lines.

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  Figure 3. Comparison between predicted and reference data. Scatterplots and Bland–Altman plots between: (A, B) the predicted Ees and the reference Ees without ejection fraction as regression input, and (C, D) the predicted Ees and the reference Ees with ejection fraction as regression input. The solid line of the scatterplots represents equality. In Bland–Altman plots, limits of agreement (LoA), within which 95% of errors are expected to lie, are defined by the two horizontal dashed lines.

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  Figure 4. Sensitivity of RMSE to changes on the training size for aortic systolic blood pressure (aSBP) (A), cardiac output (CO) (B), and end-systolic elastance (Ees) (C). RMSE root mean square error; RF random forest; SVR support vector regressor; GB gradient boosting.

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  Figure 5. Correlation matrix for the in-silico database.

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  Figure 6. Comparison between predicted and reference clinical data. Scatterplots and Bland–Altman plots between: the predicted aSBP and the reference aSBP for SVR trained using in-silico data (A, B), and for SVR trained using in-vivo data (C, D). The solid line of the scatterplots represents equality. In Bland–Altman plots, the limits of agreement (LoA), within which 95% of errors are expected to lie, are defined by the two horizontal dashed lines.

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  Figure 7. Correlation matrix for the in-vivo database.

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  Figure 8. Representation of the experimental design for the evaluation of every regression model. The model evaluation of was done using tenfold cross validation (CV) (external CV). In every external fold, we performed hyperparameter tuning with tenfold CV (internal CV).

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  Figure 9. Representation of the evaluation of the synthetically trained model against in-vivo data.