LSTM Network for Prediction of Hemorrhagic Transformation in Acute Stroke

Abstract

Hemorrhagic transformation (HT) is one of the most devastating complications of reperfusion therapy in acute ischemic stroke. Prediction of an upcoming HT remains beyond current techniques in routine clinical practice. If made available, such information would benefit the management of acute ischemic stroke patients and help to tailor therapeutic strategies. This study aims at providing a machine learning framework for predicting occurrence and extent of HT from source perfusion-weighted magnetic resonance imaging (PWI) combined with diffusion weighted imaging (DWI). The model relies on a LSTM network based on PWI combined with DWI imaging features into a fully connected neural network. A retrospective comparative analysis performed on 155 acute stroke patients demonstrate the efficacy of the LSTM model (AUC-ROC: \(89.4\%\)) against state-of-the-art machine learning models. Predicted likelihood of HT at the voxel level was evaluated against HT annotations of stroke neurologists obtained from follow-up gradient recalled echo (GRE) imaging.