Prediction of Post-Stroke Brain Swelling Using Biomechanical Modelling and Deep Neural Networks

Abstract

• Malignant stroke is a life-threatening condition, with mortality rates reaching up to 80% among patients managed conservatively. • We developed a novel computational approach that combines advanced biomechanical modelling with deep neural network (DNN) for predicting brain swelling following stroke. • Using in-silico simulations of 3,000 stroke cases, our DNN model demonstrated robust learning of anatomical features, achieving minimal errors in brain swelling prediction. • Without the need for retraining or fine-tuning, our model achieved clinically comparable performance for predicting 3-month stroke outcomes. • To the best of our knowledge, this presents the one of few pioneering computational workflows specifically designed for brain swelling prediction. Malignant stroke is a life-threatening condition, with mortality rates reaching up to 80% among patients managed conservatively. Brain swelling volume and midline shift are pivotal clinical markers for predicting stroke outcomes. However, brain oedema typically peaks two to five days post-stroke onset, which significantly delays the implementation of timely interventions. Early prediction of these markers is therefore critical for enhancing treatment strategies and improving patient outcomes. Predicting brain swelling is inherently challenging due to its biomechanical complexity, governed by factors such as lesion size and location. In this study, we proposed a novel physics-informed computational approach that combines advanced biomechanical modelling with deep neural networks (DNNs) for predicting brain swelling following stroke. Using in-silico simulations of 3,000 stroke cases generated from a poroelasticity-based mathematical model, our DNN model demonstrated robust learning of anatomical features, achieving minimal errors in brain swelling predictions for hold-out test cases. Furthermore, we externally validated the trained model using clinical imaging data from 60 stroke patients; without the need for retraining or fine-tuning, the model achieved clinically comparable outcomes, with an area under the curve (AUC) of approximately 0.7 for predicting 3-month stroke outcomes. Our findings underscore the transformative potential of this physics informed neural networks to accelerate clinical decision-making and improve the management of malignant stroke. By enabling earlier and more precise predictions of critical imaging markers, this pipeline provides a significant step forward in reducing the mortality and disability associated with this devastating condition.

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