@inproceedings{15dfae7384dc48a3b2538be00db9ea67,
title = "Automatically Generated Cardiovascular Digital Twin in Critical Care: A Proof of Concept Study",
abstract = "This proof of concept study demonstrates the capabilities of a virtually automatically generated digital twin framework for enhancing hemodynamic monitoring in critical care. By combining a deterministic cardiovascular model with patient-specific data through data assimilation techniques, the digital twin can act as a data denoiser, reconstruct physiological waveforms that are typically unavailable in critical care settings and generate clinically relevant biomarkers. Validation was performed using real data from patients under general anesthesia. The proposed framework efficient calibration and ability to follow the patient{\textquoteright}s state over time supports the possibility of real-time bedside applications.",
keywords = "Cardiovascular, Critical care monitoring, Digital twin, Model-data interaction",
author = "Fran{\c c}ois Kimmig and \{Le Gall\}, Arthur and Camille Windsor and Fabrice Vall{\'e}e and Dominique Chapelle and Philippe Moireau",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; 13th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2025 ; Conference date: 01-06-2025 Through 05-06-2025",
year = "2025",
month = jan,
day = "1",
doi = "10.1007/978-3-031-94562-5\_35",
language = "English",
isbn = "9783031945618",
series = "Lecture Notes in Computer Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "384--396",
editor = "Radom{\'i}r Chabiniok and Qing Zou and Tarique Hussain and Nguyen, \{Hoang H.\} and Zaha, \{Vlad G.\} and Maria Gusseva",
booktitle = "Functional Imaging and Modeling of the Heart - 13th International Conference, FIMH 2025, Proceedings",
}