ArticleActa neurochirurgica. Supplement2022
Machine Learning and Intracranial Aneurysms: From Detection to Outcome Prediction.
Article in Acta neurochirurgica. Supplement, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- The Construction and Validation of a new Predictive Model for Overall Survival of Clear Cell Renal Cell Carcinoma Patients with Bone Metastasis Based on Machine Learning Algorithm.Technology in cancer research & treatmentTrial
- Development and validation of an interpretable Random Forest model for predicting recurrence after endoscopic submucosal dissection in superficial oesophageal squamous cell carcinoma.Annals of medicine · 2026Article
- Predictors of Outcome Clusters in Patients With Unruptured Intracranial Aneurysms Treated With Microsurgery: An Unsupervised Machine Learning Analysis.Neurosurgery practice · 2026Article
- Comparison of the predictive performance of machine learning and conventional logistic regression models for poor discharge outcomes in patients with Aneurysmal subarachnoid hemorrhage: A retrospective cohort study.Neurosurgical review · 2026Article
- Assessing the Accuracy of Artificial Intelligence in Detecting Intracranial Aneurysms in a Clinical Setting Relative to Neuroradiologists.AJNR. American journal of neuroradiology · 2025Article
- Development and Validation of a Machine Learning‑Based Predictive Model for Assessing the Risk of Comorbid Depression in Patients With Asthma.Alpha psychiatry · 2025Article
- Multiple automated machine-learning prediction models for postoperative reintubation in patients with acute aortic dissection: a multicenter cohort study.Frontiers in medicine · 2025Article
- Construction of a prediction model for sarcopenic obesity based on machine learning.Frontiers in public health · 2025Article
- Correlation of triglyceride-glucose index with the incidence and prognosis of hyperglycemic crises in critically ill patients with diabetes mellitus: a machine-learning-based multicenter retrospective cohort study.Frontiers in nutrition · 2025Article
- Integrating PointNet-Based Model and Machine Learning Algorithms for Classification of Rupture Status of IAs.Bioengineering (Basel, Switzerland) · 2024Article
- Development and validation of a machine learning-based predictive model for assessing the 90-day prognostic outcome of patients with spontaneous intracerebral hemorrhage.Journal of translational medicine · 2024Article
- Construction of a poor prognosis prediction and visualization system for intracranial aneurysm endovascular intervention treatment based on an improved machine learning model.Frontiers in neurology · 2024Article
- Machine learning based outcome prediction of microsurgically treated unruptured intracranial aneurysms.Scientific reports · 2023Article
- Timing and outcome of bystanders treatment in patients with subarachnoid hemorrhage associated with multiple aneurysms.Neurosurgical review · 2022Article
- Development of a Machine Learning-Based Predictive Model for Lung Metastasis in Patients With Ewing Sarcoma.Frontiers in medicine · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) is a rapidly rising research tool in biomedical sciences whose applications include segmentation, classification, disease detection, and outcome prediction. With respect to traditional statistical methods, ML algorithms have the potential to learn and improve their predictive performance when fed with large data sets without the need of being specifically programmed. In recent years, this technology has been increasingly applied for tackling clinical issues in intracranial aneurysm (IA) research. Several studies attempted to provide reliable models for enhanced aneurysm detection. Convolutional neural networks trained with variable degrees of human interaction on data from diverse imaging modalities showed high sensitivity in aneurysm detection tasks, also outperforming expert image analysis. Algorithms were also shown to differentiate ruptured from unruptured IAs, with however limited clinical relevance. For prediction of rupture and stability assessment, ML was preliminarily shown to achieve better performance compared to conventional statistical methods and existing risk scores. ML-based complication and functional outcome prediction in the event of SAH have been more extensively reported, in contrast with periprocedural outcome investigation in unruptured IA patients. ML has the potential to be a game changer in IA patient management. Currently clinical translation of experimental results is limited.
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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.