An automatic deep-learning approach for the prediction of post-stroke epilepsy after an initial intracerebral hemorrhage based on non-contrast computed tomography imaging.

Background: Post-stroke epilepsy (PSE) is a common and significant complication that often occurs after stroke, and affects patients' prognosis and overall quality of life. In recent years, non-contrast computed tomography (NCCT) has become the preferred method for the clinical diagnosis of intracerebral hemorrhage (ICH). This study aimed to develop and validate a triple deep-learning model, simply named, the post-stroke epilepsy network (PSENet), to predict PSE in ICH patients based on NCCT.

Methods: A total of 1,130 patients (62 with PSE and 1,068 without PSE) who experienced an initial ICH at our hospital were enrolled in this study. Using five-fold cross-validation, all patients were randomly divided into training and validation sets at a ratio of 4:1. Next, the no-new-Net (nnU-Net) was used to automatically segment the ICH for the subsequent quantitative analysis. A triple deep-learning model was developed to extract the PSE-related features and incorporate the deep-learning features related to cortical involvement (FCI) and ICH volume to predict PSE. This model was compared with three clinical models constructed using random forest. Model performance was mainly evaluated using the area under the curve (AUC).

Results: The nnU-Net had a high Dice score of 0.923. The proposed PSENet, which incorporated multiple features, showed excellent diagnostic performance, and had an accuracy of 0.876, a F1-score of 0.621, a recall of 0.716, a specificity of 0.897, and an AUC of 0.840, which significantly surpassed the AUC of the baseline clinical model (AUC =0.787).

Conclusions: Based on our findings, the developed PSENet could be used to predict PSE quickly after the first ICH, especially in scenarios in which reliable clinical information is lacking on admission.