Abstract
The purpose of this study was to survey the existing artificial intelligence (AI) algorithms created for the automated detection of the diffusion-weighted imaging (DWI)–fluid-attenuated inversion recovery (FLAIR) mismatch and assess how their performance compares to that diagnostic techniques performed by neuroradiologists. The literature search for this systematic review was conducted in PubMed, MEDLINE, Ovid Embase, Web of Science, Scopus, and Cochrane databases up until February 2, 2024. The review team cross-checked the reference lists of the included studies to identify any additional relevant references, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We assessed the included studies using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. The area under the curve (AUC) was reported in most studies, However, one study did not report this metric, The AI models achieved AUCs between 0.60 and 0.90, Sensitivity ranged from 0.6 to 0.9, and specificity ranged from 0.72 to 0.99, the positive predictive value (PPV), negative predictive value (NPV) and F1-Score were ranging from 0.72 to 0.93, 0.47 to 0.91 and 0.65 to 0.9, respectively. Additionally, the dice similarity coefficients (DSC) 0.73 & 0.8 were stated in two researches and accuracies ranging from 0.67 to 0.99. This review indicates that the current AI methods for DWI/FLAIR mismatch assessment may not be able to accurately determine the time since stroke onset based only on the DWI and FLAIR sequences, however, an AI-based approach focused on treatment eligibility, outcome prediction, and incorporating patient-specific information could potentially improve the care of stroke patients.