SynthesisIET systems biology
The Accuracy in Rupture Risk Prediction of Intracranial Aneurysms by Artificial Intelligence Algorithms Using Imaging Data From CTA and DSA: A Systematic Review and Meta-Analysis.
Synthesis in IET systems biology. 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.
- Pooled it
- Radiomics and deep learning for intracranial aneurysm rupture-status assessment on CTA/MRA: a multicenter retrospective study.Frontiers in neuroscience · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Ruptured intracranial aneurysms (IAs) are the leading cause of aSAH. There are limitations in combining traditional imaging methods (CTA and DSA) and clinical scores (PHASES) to predict IAs rupture risk, whereas artificial intelligence (AI) algorithms show potential. This meta-analysis evaluated AI algorithm performance for predicting IAs rupture risk based on CTA and DSA. As of February 2025, we searched Web of Science, PubMed, Scopus, and Embase, extracting TP, FP, FN, and TN from included studies. The combined sensitivity, specificity, and AUC were synthesised with a bivariate random-effects model. Subgroup analyses were performed. PROSPERO: CRD420251008866. Twenty studies (13,232 patients, 14,344 IAs) reported pooled sensitivity 0.84 (95% CI: 0.80-0.87), specificity 0.82 (95% CI: 0.78-0.86), and AUC 0.90 (95% CI: 0.87-0.92) with substantial heterogeneity. Subgroup analyses showed DOR in the DSA versus CTA groups (DSA 23.55, CTA 22.21) with persistent heterogeneity. The clinical-morphological-radiomics group had DOR 18.76 without heterogeneity. By publication year, 2021 group had a lower DOR (12.99) versus 2022 (23.03) versus 2023 (26.98), with low heterogeneity. AI algorithms predicting IAs rupture risk based on CTA and DSA demonstrate high diagnostic accuracy and have potential to advance the field.
Indexed as
Identifiers
What OpenQuestion holds
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.