SynthesisJournal of neurointerventional surgery2025
What can we learn from machine learning studies on flow diverter aneurysm embolization? A systematic review.
Synthesis in Journal of neurointerventional surgery, 2025. 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
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
1 citing paper in PubMed.
- Clinical outcome prediction of Contour Neurovascular System treatment based on pre-treatment geometric and hemodynamic characteristics.International journal of computer assisted radiology and surgery · 2026Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
backgroundAs the use of flow diverters has expanded in recent years, predicting successful outcomes has become more challenging for certain aneurysms.
objectiveTo provide neurointerventionalists with an understanding of the available machine learning algorithms for predicting the success of flow diverters in occluding aneurysms.
methodsThis study followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and the four major medical databases (PubMed, Embase, Scopus, Web of Science) were screened. The study included original research articles that evaluated the predictive abilities of various machine learning algorithms for determining the success of flow diverters in achieving aneurysm occlusion.
resultsFive studies out of 217 were included based on our criteria. The included studies used various variables (patient demographics, aneurysm and parent artery characteristics, flow diverter and hemodynamic-related features, and angiographic parametric imaging) to predict flow diverter treatment outcomes. The machine learning algorithms used, along with their respective accuracy rates, were as follows: logistic regression (61% and 85%), support vector machine (88%), Gaussian support vector machine (90%), linear support vector machine (85%), decision tree (80%), random forest (87%), k-nearest neighbors (83% and 85%), XGBoost (87%), CatBoost (86%), deep neural networks (77.9%), and recurrent neural networks (74%).Two studies trained the machine learning models with both all features and the most significant features. Both studies observed that the accuracy of machine learning models decreased by removing the insignificant features.
conclusionThe current literature indicates that machine learning algorithms can be trained to predict the success of flow diverters with an accuracy of up to 90%.
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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.