In recent years, there has been significant interest in evaluating perivascular spaces (PVS) due to their potential to characterize multiple neurological conditions. In this study, we demonstrated the potential to improve PVS evaluation at scale by introducing an AI algorithm to review identified PVS candidates and remove false positives on T2-weighted MRI. For this task, we were able to achieve an AUC of 0.93 +/- 0.02 while identifying optimal model characteristics and exploring areas of future improvement and investigation, thus demonstrating the potential for AI to replace human review in PVS quantification at scale.
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