Related Experiment Video
Updated: May 5, 2026
17:06
Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
Published on: November 8, 2012
26.4K
Automated DWI-FLAIR mismatch assessment in stroke using DWI only
Joseph Benzakoun1,2,3, Lauranne Scheldeman3,4, Anke Wouters3,4
1IMA-BRAIN, INSERM U1266, Institute of Psychiatry and Neuroscience of Paris (IPNP), Université Paris Cité, Paris, France.
European Stroke Journal
|August 28, 2025
Summary
A deep-learning model accurately predicts Diffusion-Weighted Imaging-Fluid-Attenuated Inversion-Recovery mismatch in Acute Ischemic Stroke patients using only DWI data. This tool aids in identifying candidates for thrombolysis when stroke onset is unknown.
Area of Science:
- Radiology
- Artificial Intelligence
- Neurology
Background:
- Diffusion-Weighted Imaging (DWI) and Fluid-Attenuated Inversion-Recovery (FLAIR) mismatch is crucial for identifying Acute Ischemic Stroke (AIS) patients eligible for thrombolysis, especially when stroke onset is unknown.
- Visual assessment of DWI-FLAIR mismatch has limitations due to suboptimal observer agreement, impacting treatment decisions for approximately 15% of AIS cases.
Purpose of the Study:
- To develop and validate a Deep-Learning (DL) model capable of predicting DWI-FLAIR mismatch using only DWI data.
- To improve the accuracy and consistency of DWI-FLAIR mismatch identification in AIS patients.
Main Methods:
- A retrospective study utilizing AIS patient data from the ETIS registry (derivation) and WAKE-UP trial (validation).
- A DL model was trained to predict FLAIR Visible Areas (FVA) using only DWI input, defining an FVA-index.
- Model performance was evaluated using Area Under the ROC Curve (AUC) and optimal FVA-index cutoff for predicting DWI-FLAIR mismatch.
Main Results:
- The DL model demonstrated strong predictive value in both derivation (AUC=0.85) and validation cohorts (AUC=0.86).
- An optimal FVA-index cutoff of 0.5 achieved 70% sensitivity and 88% specificity in the validation cohort.
- The model showed good agreement with visual ratings, indicated by a kappa of 0.54.
Conclusions:
- The developed DL model accurately predicts DWI-FLAIR mismatch in AIS patients with unknown stroke onset.
- This AI tool can assist clinicians in challenging visual assessments or when FLAIR sequences are unavailable, potentially optimizing thrombolysis decisions.
