Interdisciplinary cardiovascular and thoracic surgery · 2026
Building an Extracorporeal Membrane Oxygenation Digital Twin Using High-Resolution Patient Data: An artificial intelligence model for virtual reality simulation.
СтатьяНазвание и аннотация: Перевод в очереди
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Max Samuel, Bourass Mounir, van der Mee Mendes Andre, van der Mee Mendes Daniel, Schalkwijk Bram, Babar Zaheer, Lim Lydia, Elzo Kraemer Carlos, Kaufman Brian, van Dijk Antony, Brekelmans Renske, Westbroek Priscilla, Mostafa Ali Abdelrhman, Hugo Juan, Klautz Robert, Braun Jerry, Mahtab Edris
Аннотация
OBJECTIVES: Extracorporeal membrane oxygenation (ECMO) is a life-saving therapy for severe cardiopulmonary failure, but structured training remains constrained by costs, logistics, and the absence of validated high-fidelity simulators. This study aimed to develop an ECMO digital twin capable of supporting training in virtual reality (VR).
METHODS: We integrated high-frequency ECMO machine data with electronic health record information from 335 patients across two centres. Data streams were synchronised at a 30-second resolution. A hierarchical two-stage system was designed: Model 1 predicted ECMO device outputs, while Model 2 combined those outputs with patient vital functions such as heart rate and blood pressure. This model was integrated into a VR simulation and underwent testing by 21 experts.
RESULTS: Model 2 demonstrated Root Mean Square Errors (RMSE) of 15.23 mmHg (diastolic arterial blood pressure), 19.50BPM (heart rate), 2.94% (peripheral oxygen saturation), and 1.42 mmHg (end-tidal carbon dioxide) on the test set. Neural networks produced clinically coherent predictions. The models were implemented in an Unreal Engine-based VR simulator using the open neural network exchange format, with real-time latency inference and scenario switching. Expert testing confirmed good performance and clinically plausible physiological responses in the simulation.
CONCLUSIONS: High-resolution ECMO data can be transformed into a digital twin for VR training. This framework broadens access to advanced ECMO education and establishes a foundation for multicentre validation, federated learning, and future expansion towards a critical-care digital-twin platform.
METHODS: We integrated high-frequency ECMO machine data with electronic health record information from 335 patients across two centres. Data streams were synchronised at a 30-second resolution. A hierarchical two-stage system was designed: Model 1 predicted ECMO device outputs, while Model 2 combined those outputs with patient vital functions such as heart rate and blood pressure. This model was integrated into a VR simulation and underwent testing by 21 experts.
RESULTS: Model 2 demonstrated Root Mean Square Errors (RMSE) of 15.23 mmHg (diastolic arterial blood pressure), 19.50BPM (heart rate), 2.94% (peripheral oxygen saturation), and 1.42 mmHg (end-tidal carbon dioxide) on the test set. Neural networks produced clinically coherent predictions. The models were implemented in an Unreal Engine-based VR simulator using the open neural network exchange format, with real-time latency inference and scenario switching. Expert testing confirmed good performance and clinically plausible physiological responses in the simulation.
CONCLUSIONS: High-resolution ECMO data can be transformed into a digital twin for VR training. This framework broadens access to advanced ECMO education and establishes a foundation for multicentre validation, federated learning, and future expansion towards a critical-care digital-twin platform.