Efficient Generation of Pretraining Samples for Developing a Deep Learning Brain Injury Model via Transfer Learning

Lin, Nan; Wu, Shaoju; Wu, Zheyang; Ji, Songbai

doi:10.1007/s10439-023-03354-3

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2024

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Cited by 2 publications

References 34 publications

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A computational pipeline towards large-scale and multiscale modeling of traumatic axonal injury

Zhang,

Bartels,

Clansey

et al. 2024

Computers in Biology and Medicine

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A computational pipeline towards large-scale and multiscale modeling of traumatic axonal injury

Zhang,

Bartels,

Clansey

et al. 2024

Computers in Biology and Medicine

View full text Add to dashboard Cite

AI-assisted In-silico Trial for the Optimization of Osmotherapy following Ischaemic Stroke

Chen,

Lu,

Józsa

et al. 2024

Preprint

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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.

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Efficient Generation of Pretraining Samples for Developing a Deep Learning Brain Injury Model via Transfer Learning

Cited by 2 publications

References 34 publications

A computational pipeline towards large-scale and multiscale modeling of traumatic axonal injury

A computational pipeline towards large-scale and multiscale modeling of traumatic axonal injury

AI-assisted In-silico Trial for the Optimization of Osmotherapy following Ischaemic Stroke

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