Homogeneous application of imaging criteria in a multicenter trial supported by investigator training: A report from the WAKE-UP study

Artificial Intelligence for Automated DWI/FLAIR Mismatch Assessment on Magnetic Resonance Imaging in Stroke: A Systematic Review

We conducted this Systematic Review to create an overview of the currently existing Artificial Intelligence (AI) methods for Magnetic Resonance Diffusion-Weighted Imaging (DWI)/Fluid-Attenuated Inversion Recovery (FLAIR)-mismatch assessment and to determine how well DWI/FLAIR mismatch algorithms perform compared to domain experts. We searched PubMed Medline, Ovid Embase, Scopus, Web of Science, Cochrane, and IEEE Xplore literature databases for relevant studies published between 1 January 2017 and 20 November 2022, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We assessed the included studies using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Five studies fit the scope of this review. The area under the curve ranged from 0.74 to 0.90. The sensitivity and specificity ranged from 0.70 to 0.85 and 0.74 to 0.84, respectively. Negative predictive value, positive predictive value, and accuracy ranged from 0.55 to 0.82, 0.74 to 0.91, and 0.73 to 0.83, respectively. In a binary classification of ±4.5 h from stroke onset, the surveyed AI methods performed equivalent to or even better than domain experts. However, using the relation between time since stroke onset (TSS) and increasing visibility of FLAIR hyperintensity lesions is not recommended for the determination of TSS within the first 4.5 h. An AI algorithm on DWI/FLAIR mismatch assessment focused on treatment eligibility, outcome prediction, and consideration of patient-specific data could potentially increase the proportion of stroke patients with unknown onset who could be treated with thrombolysis.

Which Imaging Approach Should Be Used for Stroke of Unknown Time of Onset?

Reperfusion therapy with intravenous thrombolysis or mechanical thrombectomy is effective in improving outcome for ischemic stroke but remains underused. Patients presenting with stroke of unknown onset are a common clinical scenario and a common reason for not offering reperfusion therapy. Recent studies have demonstrated the efficacy of reperfusion therapy in stroke of unknown time of onset, when guided by advanced brain imaging. However, translation into clinical practice is challenged by variability in the available data. Comparison between studies is difficult because of use of different imaging modalities (magnetic resonance imaging or computed tomography), different imaging paradigms (imaging biomarkers of lesion age versus imaging biomarkers of tissue viability), and different populations studied (ie, both patients with large vessel occlusion or those with less severe strokes). Physicians involved in acute stroke care are faced with the key question of which imaging approach they should use to guide reperfusion treatment for stroke with unknown time of onset. In this review, we provide an overview of the available evidence for selecting and treating patients with strokes of unknown onset, based on the underlying imaging concepts. The perspective provided is from the viewpoint of the clinician seeing these patients acutely, to provide pragmatic recommendations for clinical practice.