AI-assisted in silico trial for the optimization of osmotherapy after ischaemic stroke

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dc.contributor.authorChen, Xi
dc.contributor.authorLu, Lei
dc.contributor.authorJózsa, Tamás I.
dc.contributor.authorZhou, Jiandong
dc.contributor.authorClifton, David A.
dc.contributor.authorPayne, Stephen J.
dc.date.accessioned2025-03-03T10:58:17Z
dc.date.available2025-03-03T10:58:17Z
dc.date.freetoread2025-03-03
dc.date.issued2025
dc.date.pubOnline2025-02-12
dc.description.abstractOver 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.
dc.description.journalNameIEEE Journal of Biomedical and Health Informatics
dc.description.sponsorshipStephen J. Payne is supported by a Yushan Fellowship from the Ministry of Education, Taiwan (111V1004-2). David A. Clifton is supported by the Pandemic Sciences Institute at the University of Oxford; the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC); an NIHR Research Professorship; a Royal Academy of Engineering Research Chair; and the InnoHK Hong Kong Centre for Centre for Cerebro-cardiovascular Engineering (COCHE).
dc.format.extentpp. xx-xx
dc.identifier.citationChen X, Lu L, Józsa TI, et al., (2025) AI-assisted in silico trial for the optimization of osmotherapy after ischaemic stroke. IEEE Journal of Biomedical and Health Informatics, Available online 12 February 2025
dc.identifier.eissn2168-2208
dc.identifier.elementsID564708
dc.identifier.issn2168-2194
dc.identifier.issueNoahead-of-print
dc.identifier.urihttps://doi.org/10.1109/jbhi.2025.3541004
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23556
dc.identifier.volumeNoahead-of-print
dc.language.isoen
dc.publisherIEEE
dc.publisher.urihttps://ieeexplore.ieee.org/document/10882905
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject46 Information and Computing Sciences
dc.subject4601 Applied Computing
dc.subjectStroke
dc.subjectNeurosciences
dc.subjectClinical Research
dc.subjectNetworking and Information Technology R&D (NITRD)
dc.subjectBioengineering
dc.subjectClinical Trials and Supportive Activities
dc.subjectBrain Disorders
dc.subjectCerebrovascular
dc.subjectMachine Learning and Artificial Intelligence
dc.subject6.1 Pharmaceuticals
dc.subjectStroke
dc.titleAI-assisted in silico trial for the optimization of osmotherapy after ischaemic stroke
dc.typeArticle
dcterms.dateAccepted2025-02-07

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