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Barakat CS, Sharafutdinov K, Busch J, Saffaran S, Bates DG, Hardman JG, Schuppert A, Brynjólfsson S, Fritsch S, Riedel M. Developing an Artificial Intelligence-Based Representation of a Virtual Patient Model for Real-Time Diagnosis of Acute Respiratory Distress Syndrome. Diagnostics (Basel) 2023; 13:2098. [PMID: 37370993 DOI: 10.3390/diagnostics13122098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 06/10/2023] [Accepted: 06/15/2023] [Indexed: 06/29/2023] Open
Abstract
Acute Respiratory Distress Syndrome (ARDS) is a condition that endangers the lives of many Intensive Care Unit patients through gradual reduction of lung function. Due to its heterogeneity, this condition has been difficult to diagnose and treat, although it has been the subject of continuous research, leading to the development of several tools for modeling disease progression on the one hand, and guidelines for diagnosis on the other, mainly the "Berlin Definition". This paper describes the development of a deep learning-based surrogate model of one such tool for modeling ARDS onset in a virtual patient: the Nottingham Physiology Simulator. The model-development process takes advantage of current machine learning and data-analysis techniques, as well as efficient hyperparameter-tuning methods, within a high-performance computing-enabled data science platform. The lightweight models developed through this process present comparable accuracy to the original simulator (per-parameter R2 > 0.90). The experimental process described herein serves as a proof of concept for the rapid development and dissemination of specialised diagnosis support systems based on pre-existing generalised mechanistic models, making use of supercomputing infrastructure for the development and testing processes and supported by open-source software for streamlined implementation in clinical routines.
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Affiliation(s)
- Chadi S Barakat
- Jülich Supercomputing Centre, Forschungszentrum Jülich, 52428 Jülich, Germany
- School of Engineering and Natural Science, University of Iceland, 107 Reykjavik, Iceland
- SMITH Consortium of the German Medical Informatics Initiative, 07747 Leipzig, Germany
| | - Konstantin Sharafutdinov
- SMITH Consortium of the German Medical Informatics Initiative, 07747 Leipzig, Germany
- Joint Research Centre for Computational Biomedicine, University Hospital RWTH Aachen, 52074 Aachen, Germany
| | - Josefine Busch
- Jülich Supercomputing Centre, Forschungszentrum Jülich, 52428 Jülich, Germany
| | - Sina Saffaran
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK
| | - Declan G Bates
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK
| | | | - Andreas Schuppert
- SMITH Consortium of the German Medical Informatics Initiative, 07747 Leipzig, Germany
- Joint Research Centre for Computational Biomedicine, University Hospital RWTH Aachen, 52074 Aachen, Germany
| | - Sigurður Brynjólfsson
- School of Engineering and Natural Science, University of Iceland, 107 Reykjavik, Iceland
| | - Sebastian Fritsch
- Jülich Supercomputing Centre, Forschungszentrum Jülich, 52428 Jülich, Germany
- SMITH Consortium of the German Medical Informatics Initiative, 07747 Leipzig, Germany
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, 52074 Aachen, Germany
| | - Morris Riedel
- Jülich Supercomputing Centre, Forschungszentrum Jülich, 52428 Jülich, Germany
- School of Engineering and Natural Science, University of Iceland, 107 Reykjavik, Iceland
- SMITH Consortium of the German Medical Informatics Initiative, 07747 Leipzig, Germany
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