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

SUBMITTER: Kim PJ 

PROVIDER: S-EPMC11836310 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature