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TRV-2026-1149Version 1 · Certified

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TRUVACE RECORD VERSION
record: TRV-2026-1149
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-20T06:53:32.779245Z
status: published
lens: p_space
sector: health
headline: Artificial Intelligence in Cerebral Small Vessel Disease Imaging: A Study-Level Cross-Sectional Analysis of Validation Status and Clinical Applicability
dek: 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…
gain_title: (none)
problem_title: Artificial Intelligence in Cerebral Small Vessel Disease Imaging: A Study-Level Cross-Sectional Analysis of Validation Status and Clinical Applicability: 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%).
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Artificial Intelligence in Cerebral Small Vessel Disease Imaging: A Study-Level Cross-Sectional Analysis of Validation Status and Clinical Applicability: 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%).
problem_evidence: (none)
quick_read: 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.

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%).
limitation: 
tag: Evidence-backed problem
key_points: 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.
rundown: 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%).
sources:
- peer_reviewed | Journal of Imaging Informatics in Medicine | https://doi.org/10.1007/s10278-026-02327-x | 2026-09-18
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