Artificial Intelligence in Cerebral Small Vessel Disease Imaging: A Study-Level Cross-Sectional Analysis of...

Artificial intelligence (AI) is increasingly used in cerebral small vessel disease (CSVD) imaging, but the extent of validation and clinical applicability across the literature remains uncertain. We performed a study-level cross-sectional analysis using a frozen Web of Science Core Collection (WoSCC) cohort supplemented by PubMed and IEEE Xplore searches to assess database coverage and the stability of findings from the WoSCC cohort. After study-level reconciliation, 463 independent studies were included. White matter hyperintensity segmentation/quantification was the most common task (196/463, 42.3%), followed by cerebral microbleed detection/classification (82/463, 17.7%), perivascular space/lacune assessment (67/463, 14.5%), CSVD burden/risk modeling (62/463, 13.4%), and clinical outcome prediction (56/463, 12.1%). Deep learning was the largest model family (185/463, 40.0%). Higher validation levels (L4-L5) were reached by 146/462 studies (31.6%), external or multicenter validation was reported in 147/463 (31.7%), and higher clinical applicability evidence was identified in 159/462 (34.4%). Calibration (51/438, 11.6%), decision curve or net-benefit analysis (40/436, 9.2%), and reader-, workflow-, or implementation-based evaluation (34/436, 7.8%) remained uncommon. Broader database coverage materially changed the task and model composition of the evidence base, particularly by increasing the representation of white matter hyperintensity and image-processing studies, but did not alter the central finding that high-level validation and clinically oriented evaluation remain limited.