ArticleNeurosurgery practice2026
Predictors of Outcome Clusters in Patients With Unruptured Intracranial Aneurysms Treated With Microsurgery: An Unsupervised Machine Learning Analysis.
Article in Neurosurgery practice, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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
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Who cites it
1 citing paper in PubMed.
- Artificial Intelligence for Cerebral Aneurysm Management: Integrating Imaging, Hemodynamics, and Clinical Decision Support.Journal of imaging informatics in medicine · 2026Review
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectivesIdentifying surgical candidates who are prone to poor outcomes is crucial for adapting treatment and ensuring optimal outcomes. Our aim was to use unsupervised machine learning to reveal patient outcome subgroups and identify predictors of attribution to these subgroups in patients with unruptured intracranial aneurysms.
methodsWe retrospectively reviewed 400 patients who underwent microsurgical treatment for unruptured aneurysms at a large quaternary center from January 1, 2014, to December 31, 2020. Evaluated outcomes included new neurological deficits at discharge and poor neurological outcome at follow-up. Outcomes were clustered using a k-means model after the number of optimal clusters was obtained using a total within sum of squares algorithm. Uniform manifold approximation and projection was used to project the outcomes into a 2-dimensional space. On cluster determination, multivariate regression with a
resultsThree unique clusters were identified. Cluster 1 represented patients who had no poor neurological outcome (0/142, 0%) and no neurological deficits at discharge (0%). Cluster 2 represented patients with a high burden of new neurological deficit at discharge (76/143 [53%]) but no poor neurological outcome (0%). Cluster 3 represented patients with a new neurological deficit at discharge (52/115 [45%],
conclusionUnsupervised machine learning grouped outcomes into 3 distinct, clinically meaningful clusters. Vascular comorbidities, rather than aneurysm characteristics, significantly predicted attribution to outcome clusters.
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