AIMC Topic: Aneurysm, Ruptured

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A Comparative Classification Analysis of Abdominal Aortic Aneurysms by Machine Learning Algorithms.

Annals of biomedical engineering
The objective of this work was to perform image-based classification of abdominal aortic aneurysms (AAA) based on their demographic, geometric, and biomechanical attributes. We retrospectively reviewed existing demographics and abdominal computed tom...

Comparison of statistical learning approaches for cerebral aneurysm rupture assessment.

International journal of computer assisted radiology and surgery
PURPOSE: Incidental aneurysms pose a challenge to physicians who need to decide whether or not to treat them. A statistical model could potentially support such treatment decisions. The aim of this study was to compare a previously developed aneurysm...

Prediction of Aneurysm Stability Using a Machine Learning Model Based on PyRadiomics-Derived Morphological Features.

Stroke
Background and Purpose- Discrimination of the stability of intracranial aneurysms is critical for determining the treatment strategy, especially in small aneurysms. This study aims to evaluate the feasibility of applying machine learning for predicti...

Machine Learning Models can Detect Aneurysm Rupture and Identify Clinical Features Associated with Rupture.

World neurosurgery
BACKGROUND: Machine learning (ML) has been increasingly used in medicine and neurosurgery. We sought to determine whether ML models can distinguish ruptured from unruptured aneurysms and identify features associated with rupture.

Use of artificial neural networks to predict anterior communicating artery aneurysm rupture: possible methodological considerations.

European radiology
Use of algorithms to generate synthetic cases might result in a misrepresentation of the entire population. Training an artificial neural network with a mix of real and synthetic data might lead to non-realistic prediction precision.

Prediction of rupture risk in anterior communicating artery aneurysms with a feed-forward artificial neural network.

European radiology
OBJECTIVES: Anterior communicating artery (ACOM) aneurysms are the most common intracranial aneurysms, and predicting their rupture risk is challenging. We aimed to predict this risk using a two-layer feed-forward artificial neural network (ANN).

Predicting symptomatic cerebral vasospasm after aneurysmal subarachnoid hemorrhage with an artificial neural network in a pediatric population.

Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery
PURPOSE: Artificial neural networks (ANN) are increasingly applied to complex medical problem solving algorithms because their outcome prediction performance is superior to existing multiple regression models. ANN can successfully identify symptomati...

Prospective Assessment of a Symptomatic Cerebral Vasospasm Predictive Neural Network Model.

World neurosurgery
INTRODUCTION: The author introduced a symptomatic cerebral vasospasm (SCV) prediction model built with freeware based on a 91-patient dataset. In a prospective test group of 22 patients at the same hospital, this model outperformed logistic regressio...

An autopsy case of cardiac tamponade caused by a ruptured ventricular aneurysm associated with acute myocarditis.

Legal medicine (Tokyo, Japan)
We report an autopsy case of hemopericardium caused by rupture of a ventricular aneurysm associated with acute myocarditis in an infant boy aged 2 years and 10 months. Three days before his death, the patient developed fever. On the day of death, he ...

Phenotype-driven risk stratification of cerebral aneurysms using Shapley Additive Explanations-based supervised clustering: a novel approach to rupture prediction.

Neurosurgical focus
OBJECTIVE: The aim of this study was to address the limitations of traditional aneurysm risk scoring systems and computational fluid dynamics (CFD) analyses by applying a supervised clustering framework to identify distinct aneurysm phenotypes and im...