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Building an Extracorporeal Membrane Oxygenation Digital Twin Using High-Resolution Patient Data: An artificial

Samuel Max¹, Mounir Bourass¹, Andre van der Mee Mendes¹

¹Department of Cardiothoracic Surgery, Leiden University Medical Center, Leiden, Netherlands.

Interdisciplinary Cardiovascular and Thoracic Surgery

|February 19, 2026

Summary

This summary is machine-generated.

This study developed a digital twin for Extracorporeal Membrane Oxygenation (ECMO) training using virtual reality. The ECMO digital twin provides a high-fidelity simulation for advanced critical care education.

Keywords:

Artificial Intelligence Digital Twin Extracorporeal Membrane Oxygenator Neural Network Training Virtual Reality

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Area of Science:

Medical Simulation
Digital Health
Cardiopulmonary Support

Background:

Extracorporeal membrane oxygenation (ECMO) is vital for severe cardiopulmonary failure.
Training is limited by simulator availability, cost, and logistics.
A high-fidelity, accessible ECMO training solution is needed.

Purpose of the Study:

To develop a digital twin of ECMO for virtual reality (VR) training.
To create a realistic and responsive simulation environment for ECMO education.

Main Methods:

Integrated high-frequency ECMO data and EHR data from 335 patients.
Developed a two-stage hierarchical model predicting ECMO outputs and patient vitals.
Implemented the model in an Unreal Engine VR simulator with real-time performance.

Main Results:

The digital twin model achieved low RMSE for key physiological parameters (e.g., BP, HR, SpO2, ETCO2).
Neural networks generated clinically coherent predictions.
Expert testing validated the simulator's performance and physiological plausibility.

Conclusions:

High-resolution ECMO data can create a functional digital twin for VR training.
This framework enhances ECMO education accessibility.
It provides a foundation for future critical care digital twin platforms and multicenter validation.