AIMC Topic: Intracranial Arteriovenous Malformations
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The application of machine learning for treatment selection of unruptured brain arteriovenous malformations: A secondary analysis of the ARUBA trial data.
Clinical neurology and neurosurgery
Dec 8, 2024
OBJECTIVE: To build a supervised machine learning (ML) model that selects the best first-line treatment strategy for unruptured bAVMs.
Artificial intelligence in the surgical management of arteriovenous malformations.
Neurosurgical review
Sep 19, 2024
Artificial intelligence-based automatic nidus segmentation of cerebral arteriovenous malformation on time-of-flight magnetic resonance angiography.
European journal of radiology
Jun 13, 2024
OBJECTIVE: Accurate nidus segmentation and quantification have long been challenging but important tasks in the clinical management of Cerebral Arteriovenous Malformation (CAVM). However, there are still dilemmas in nidus segmentation, such as diffic...
Machine Learning Models for Brain Arteriovenous Malformations Presenting with Hemorrhage Based on Clinical and Angioarchitectural Characteristics.
Academic radiology
Sep 30, 2023
RATIONALE AND OBJECTIVES: This study aims to develop the best diagnostic model for brain arteriovenous malformations (bAVMs) rupture by using machine learning (ML) algorithms.
CT Angiography Radiomics Combining Traditional Risk Factors to Predict Brain Arteriovenous Malformation Rupture: a Machine Learning, Multicenter Study.
Translational stroke research
Jun 13, 2023
This study aimed to develop a machine learning model for predicting brain arteriovenous malformation (bAVM) rupture using a combination of traditional risk factors and radiomics features. This multicenter retrospective study enrolled 586 patients wit...
Deep Learning Detection and Segmentation of Brain Arteriovenous Malformation on Magnetic Resonance Angiography.
Journal of magnetic resonance imaging : JMRI
May 23, 2023
BACKGROUND: The delineation of brain arteriovenous malformations (bAVMs) is crucial for subsequent treatment planning. Manual segmentation is time-consuming and labor-intensive. Applying deep learning to automatically detect and segment bAVM might he...
Automated segmentation of multiparametric magnetic resonance images for cerebral AVM radiosurgery planning: a deep learning approach.
Scientific reports
Jan 17, 2022
Stereotactic radiosurgery planning for cerebral arteriovenous malformations (AVM) is complicated by the variability in appearance of an AVM nidus across different imaging modalities. We developed a deep learning approach to automatically segment cere...
Intervening Nidal Brain Parenchyma and Risk of Radiation-Induced Changes After Radiosurgery for Brain Arteriovenous Malformation: A Study Using an Unsupervised Machine Learning Algorithm.
World neurosurgery
Jan 22, 2019
OBJECTIVE: To assess the sensitivity and specificity of arteriovenous malformation (AVM) nidal component identification and quantification using an unsupervised machine learning algorithm and to evaluate the association between intervening nidal brai...
Outcomes and Complications After Endovascular Treatment of Brain Arteriovenous Malformations: A Prognostication Attempt Using Artificial Intelligence.
World neurosurgery
Sep 28, 2016
PURPOSE: To identify factors influencing outcome in brain arteriovenous malformations (BAVM) treated with endovascular embolization. We also assessed the feasibility of using machine learning techniques to prognosticate and predict outcome and compar...
Humans
Artificial Intelligence
Retrospective Studies
Female
Young Adult
Adult
Follow-Up Studies
Cerebral Angiography
Male
Middle Aged
Outcome Assessment, Health Care
Intracranial Arteriovenous Malformations
Postoperative Complications
Intracranial Hemorrhages
Treatment Outcome
Endovascular Procedures
Using a Machine Learning Approach to Predict Outcomes after Radiosurgery for Cerebral Arteriovenous Malformations.
Scientific reports
Feb 9, 2016
Predictions of patient outcomes after a given therapy are fundamental to medical practice. We employ a machine learning approach towards predicting the outcomes after stereotactic radiosurgery for cerebral arteriovenous malformations (AVMs). Using th...
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