ArticleScientific reports2020
Prediction of Intracranial Aneurysm Risk using Machine Learning.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 50 citations in OpenAlex.
- Artificial Intelligence for Cerebral Aneurysm Management: Integrating Imaging, Hemodynamics, and Clinical Decision Support.Journal of imaging informatics in medicine · 2026Review
- Artificial Intelligence in Peripheral Artery Disease: A Science Advisory From the American Heart Association.Circulation. Population health and outcomes · 2026Review
- Predictors of Outcome Clusters in Patients With Unruptured Intracranial Aneurysms Treated With Microsurgery: An Unsupervised Machine Learning Analysis.Neurosurgery practice · 2026Article
- Synergistic effect of insulin resistance and glycemic variability on mortality in ICU patients with gastrointestinal bleeding: impact of the TyG-GVI index and development of an explainable web-based clinical calculator.Frontiers in endocrinology · 2026Article
- Association of the triglyceride-glucose series indices with hypertension and pre-hypertension in Anhui China.Scientific reports · 2025Article
- Decision Support Systems in Neurosurgery: Current Applications and Future Directions.Sensors (Basel, Switzerland) · 2025Review
- Assessing the Accuracy of Artificial Intelligence in Detecting Intracranial Aneurysms in a Clinical Setting Relative to Neuroradiologists.AJNR. American journal of neuroradiology · 2025Article
- Identification of Key Genes Related to Both Lipid Metabolism Disorders and Inflammation in MAFLD.Biomedicines · 2025Article
- Intracranial aneurysm CTA images and 3D models dataset with clinical morphological and hemodynamic data.Scientific data · 2024Article
- Artificial intelligence applied to development of predictive stability model for intracranial aneurysms.European journal of medical research · 2024Article
- Machine Learning-Based Prediction of Clinical Outcomes in Microsurgical Clipping Treatments of Cerebral Aneurysms.Diagnostics (Basel, Switzerland) · 2024Article
- A comprehensive investigation of morphological features responsible for cerebral aneurysm rupture using machine learning.Scientific reports · 2024Article
- Integrating PointNet-Based Model and Machine Learning Algorithms for Classification of Rupture Status of IAs.Bioengineering (Basel, Switzerland) · 2024Article
- Risk factors and predictive indicators of rupture in cerebral aneurysms.Frontiers in physiology · 2024Review
- Machine learning regression algorithms to predict short-term efficacy after anti-VEGF treatment in diabetic macular edema based on real-world data.Scientific reports · 2023Article
- Validation of prediction algorithm for risk estimation of intracranial aneurysm development using real-world data.Scientific reports · 2023Article
- A Review of Artificial Intelligence in the Rupture Risk Assessment of Intracranial Aneurysms: Applications and Challenges.Brain sciences · 2023Review
- Interpretable machine learning model to predict rupture of small intracranial aneurysms and facilitate clinical decision.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2022Article
- Predicting the rupture status of small middle cerebral artery aneurysms using random forest modeling.Frontiers in neurology · 2022Article
- Extracellular Vesicles and Their Associated miRNAs as Potential Biomarkers in Intracranial Aneurysm.Frontiers in molecular biosciences · 2022Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An efficient method for identifying subjects at high risk of an intracranial aneurysm (IA) is warranted to provide adequate radiological screening guidelines and effectively allocate medical resources. We developed a model for pre-diagnosis IA prediction using a national claims database and health examination records. Data from the National Health Screening Program in Korea were utilized as input for several machine learning algorithms: logistic regression (LR), random forest (RF), scalable tree boosting system (XGB), and deep neural networks (DNN). Algorithm performance was evaluated through the area under the receiver operating characteristic curve (AUROC) using different test data from that employed for model training. Five risk groups were classified in ascending order of risk using model prediction probabilities. Incidence rate ratios between the lowest- and highest-risk groups were then compared. The XGB model produced the best IA risk prediction (AUROC of 0.765) and predicted the lowest IA incidence (3.20) in the lowest-risk group, whereas the RF model predicted the highest IA incidence (161.34) in the highest-risk group. The incidence rate ratios between the lowest- and highest-risk groups were 49.85, 35.85, 34.90, and 30.26 for the XGB, LR, DNN, and RF models, respectively. The developed prediction model can aid future IA screening strategies.
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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.