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Parallel-plate Flow Chamber and Continuous Flow Circuit to Evaluate Endothelial Progenitor Cells under Laminar Flow Shear Stress

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Artificial Deep Neural Network for Sensorless Pump Flow and Hemodynamics Estimation During Continuous-Flow Mechanical

Taiyo Kuroda¹, Barry D Kuban¹, Takuma Miyamoto¹

¹From the Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio.

ASAIO Journal (American Society for Artificial Internal Organs : 1992)

|April 5, 2023

Summary

Artificial deep neural networks (ADNN) provide more accurate estimations of pump flow and systemic vascular resistance (SVR) compared to traditional mathematical regression models in total artificial heart research.

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Use of Two Intracorporeal Ventricular Assist Devices As a Total Artificial Heart

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Published on: May 11, 2018

Area of Science:

Biomedical Engineering
Artificial Intelligence in Medicine
Cardiovascular Devices

Background:

Accurate hemodynamic monitoring is crucial for managing patients with total artificial hearts (TAH).
Traditional mathematical models may have limitations in capturing complex physiological dynamics.
Artificial Deep Neural Networks (ADNN) offer a potential alternative for advanced data analysis.

Purpose of the Study:

To compare the accuracy of pump flow and systemic vascular resistance (SVR) estimations.
To evaluate an ADNN model against a mathematical regression model.
To assess performance using data from continuous-flow total artificial hearts (CFTAH).

Main Methods:

Generated hemodynamic and pump data using Cleveland Clinic CFTAH and pediatric CFTAH on a mock circulatory loop.
Trained an ADNN model and developed a mathematical regression model using the same dataset.
Compared absolute errors between measured data and estimations from both models.

Main Results:

Both models showed strong correlations between measured and estimated pump flow (R=0.97 for mathematical, R=0.99 for ADNN).
ADNN demonstrated significantly smaller absolute error for pump flow (0.12 L/min) compared to mathematical regression (0.3 L/min).
Similar high correlations and reduced errors were observed for Systemic Vascular Resistance (SVR) estimations with ADNN.

Conclusions:

ADNN estimation significantly outperformed mathematical regression in accuracy for both pump flow and SVR.
ADNN models show promise for enhanced hemodynamic monitoring in TAH applications.
This study supports the use of ADNN for more precise physiological parameter estimation in mechanical circulatory support.