ArticleNeurosurgical review2022
Development and assessment of machine learning models for predicting recurrence risk after endovascular treatment in patients with intracranial aneurysms.
Article in Neurosurgical review, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Systematic Review of Radiomics and Artificial Intelligence in Intracranial Aneurysm Management.Journal of neuroimaging : official journal of the American Society of NeuroimagingPooled it
- Construction and validation of machine learning models for sepsis prediction in patients with acute pancreatitis.BMC surgery · 2023Trial
- Predicting Cerebral Aneurysm Recurrence after Coil Embolization: A Novel Deep Learning Approach Using Time-of-flight Magnetic Resonance Angiography.Neurologia medico-chirurgica · 2026Article
- Prediction of Recurrence and Rupture Risk of Ruptured and Unruptured Intracranial Aneurysms of the Posterior Circulation: A Machine Learning-Based Analysis.Diagnostics (Basel, Switzerland) · 2025Article
- Identifying high-risk patients having ERCP as a day surgery with an online prediction platform: Multicohort validation of a machine learning model.Endoscopy international open · 2025Article
- A Retrospective Machine Learning Analysis to Predict 3-Month Nonunion of Unstable Distal Clavicle Fracture Patients Treated with Open Reduction and Internal Fixation.Therapeutics and clinical risk management · 2025Article
- Global trend in research of intracranial aneurysm management with artificial intelligence technology: a bibliometric analysis.Quantitative imaging in medicine and surgery · 2024Article
- Risk factors and predictive indicators of rupture in cerebral aneurysms.Frontiers in physiology · 2024Review
- A predictive model for the recurrence of intracranial aneurysms following coil embolization.Frontiers in neurology · 2023Article
- Prediction and analysis of periprocedural complications associated with endovascular treatment for unruptured intracranial aneurysms using machine learning.Frontiers in neurology · 2022Article
Corrections and comments
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Authors and funding
12 authors at 5 institutions in 2 countries.
Funding
Abstract
Intracranial aneurysms (IAs) remain a major public health concern and endovascular treatment (EVT) has become a major tool for managing IAs. However, the recurrence rate of IAs after EVT is relatively high, which may lead to the risk for aneurysm re-rupture and re-bleed. Thus, we aimed to develop and assess prediction models based on machine learning (ML) algorithms to predict recurrence risk among patients with IAs after EVT in 6 months. Patient population included patients with IAs after EVT between January 2016 and August 2019 in Hunan Provincial People's Hospital, and an adaptive synthetic (ADASYN) sampling approach was applied for the entire imbalanced dataset. We developed five ML models and assessed the models. In addition, we used SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanation (LIME) algorithms to determine the importance of the selected features and interpret the ML models. A total of 425 IAs were enrolled into this study, and 66 (15.5%) of which recurred in 6 months. Among the five ML models, gradient boosting decision tree (GBDT) model performed best. The area under curve (AUC) of the GBDT model on the testing set was 0.842 (sensitivity: 81.2%; specificity: 70.4%). Our study firstly demonstrated that ML-based models can serve as a reliable tool for predicting recurrence risk in patients with IAs after EVT in 6 months, and the GBDT model showed the optimal prediction performance.
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