ReviewNeurology research international2026
Diagnostic Test Accuracy of Artificial Intelligence in Large Vessel Occlusion: A Systematic Review and Meta-Analysis.
Review in Neurology research international, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Lung ultrasound aeration score as a predictor of extubation failure in neonates: an updated systematic review with meta-analysis and meta-regression.European journal of pediatrics · 2026Pooled it
- Diagnostic Test Accuracy of Artificial Intelligence in Large Vessel Occlusion: A Systematic Review and Meta-Analysis.Neurology research international · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large vessel occlusion (LVO) requires prompt detection, and CT angiography (CTA) is frequently used due to its short acquisition time and visibility of vessels. Artificial intelligence (AI), including Viz-LVO, CINA-LVO, RAPID-CTA and JLK, may be available as emerging tools for supporting timely and accurate diagnoses. This study aimed to examine and summarise the evidence of AI diagnostic performance in detecting LVO. Scopus, PubMed and ScienceDirect were utilised to search relevant articles before February 2, 2025. Studies were included in the primary outcomes analysis if they reported an overall confusion diagnostic matrix and were included in the secondary outcomes if they reported AI's diagnostic performance by occlusion site. Of the 878 records, 11 articles were included, and 10.937 patients were identified. The pooled sensitivity and specificity were 0.87 (95% CI: 0.76-0.93) and 0.95 (95% CI: 0.91-0.97). The positive likelihood ratio (PLR) showed statistical significance (9.55 (95% CI: 5.79-13.30;
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.