AI-assisted In silico Trial for the Optimization of Osmotherapy after Ischaemic Stroke
Journal
IEEE Journal of Biomedical and Health Informatics
Start Page
1
End Page
13
ISSN
2168-2194
2168-2208
Date Issued
2025
Author(s)
Abstract
Over the past few decades, osmotherapy has commonly been employed to reduce intracranial pressure in post-stroke oedema. However, evaluating the effectiveness of osmotherapy has been challenging due to the difficulties in clinical intracranial pressure measurement. As a result, there are no established guidelines regarding the selection of administration protocol parameters. Considering that the infusion of osmotic agents can also give rise to various side effects, the effectiveness of osmotherapy has remained a subject of debate. In previous studies, we proposed the first mathematical model for the investigation of osmotherapy and validated the model with clinical intracranial pressure data. The physiological parameters vary among patients and such variations can result in the failure of osmotherapy. Here, we propose an AI-assisted in silico trial for further investigation of the optimisation of administration protocols. The proposed deep neural network predicts intracranial pressure evolution over osmotherapy episodes. The effects of the parameters and the choice of dose of osmotic agents are investigated using the model. In addition, clinical stratifications of patients are related to a brain model for the first time for the optimisation of treatment of different patient groups. This provides an alternative approach to tackle clinical challenges with in silico trials supported by both mathematical/physical laws and patient-specific biomedical information.
Subjects
Brain ischaemic stroke
Cerebral oedema
Deep neural network
Finite Element Method
Osmotherapy
SDGs
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Type
journal article