Deep learning-based classification of diffusion-weighted imaging-fluid-attenuated inversion recovery mismatch

Abstract

The presence of a diffusion-weighted imaging (DWI)–fluid-attenuated inversion recovery (FLAIR) mismatch holds potential value in identifying candidates for recanalization treatment. However, the visual assessment of DWI–FLAIR mismatch is subject to limitations due to variability among raters, which affects accuracy and consistency. To overcome these challenges, we aimed to develop and validate a deep learning-based classifier to categorize the mismatch. We screened consecutive acute ischemic stroke patients who underwent DWI and FLAIR imaging from a four stroke centers. Two centers were used for model development and internal testing (derivation cohort), while two independent centers served as external validation cohorts. We developed Convolutional Neural Network-based classifiers for two binary classifications: DWI–FLAIR match versus non-match (Label Set I) and match versus mismatch (Label Set II). A total of 2369 patients from the derivation set and 679 patients from two external validation sets (350 and 329 patients) were included in the analysis. For Label Set I, the internal test set AUC was 0.862 (95% CI 0.841–0.884, with external validation AUCs of 0.829 (0.785–0.873) and 0.835 (0.790–0.879). Label Set II showed higher performance with internal test AUC of 0.934 (0.911–0.957) and external validation AUCs of 0.883 (0.829–0.938) and 0.913 (0.876–0.951). A deep learning-based classifier for the DWI–FLAIR mismatch can be used to diminish subjectivity and support targeted decision-making in the treatment of acute stroke patients.