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Intracranial Aneurysm

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Artificial Intelligence Prediction Model of Occurrence of Cerebral Vasospasms Based on Machine Learning.

Journal of neurological surgery. Part A, Central European neurosurgery
BACKGROUND:  Symptomatic cerebral vasospasms are deleterious complication of the rupture of a cerebral aneurysm and potentially lethal. The existing scales used to classify the initial presentation of a subarachnoid hemorrhage (SAH) offer a blink of ...

Knowledge-Augmented Deep Learning for Segmenting and Detecting Cerebral Aneurysms With CT Angiography: A Multicenter Study.

Radiology
Background Deep learning (DL) could improve the labor-intensive, challenging processes of diagnosing cerebral aneurysms but requires large multicenter data sets. Purpose To construct a DL model using a multicenter data set for accurate cerebral aneur...

Reduced order modelling of intracranial aneurysm flow using proper orthogonal decomposition and neural networks.

International journal for numerical methods in biomedical engineering
Reduced order modelling (ROMs) methods, such as proper orthogonal decomposition (POD), systematically reduce the dimensionality of high-fidelity computational models and potentially achieve large gains in execution speed. Machine learning (ML) using ...

Advancing aneurysm management: the potential of AI and machine learning in enhancing safety and predictive accuracy.

Neurosurgical review
Cerebral aneurysm rupture, the predominant cause of non-traumatic subarachnoid hemorrhage, underscores the need for effective treatment and early detection methods. A study in Neurosurgical Review compared microsurgical clipping to endovascular thera...

Reproducibility and across-site transferability of an improved deep learning approach for aneurysm detection and segmentation in time-of-flight MR-angiograms.

Scientific reports
This study aimed to (1) replicate a deep-learning-based model for cerebral aneurysm segmentation in TOF-MRAs, (2) improve the approach by testing various fully automatic pre-processing pipelines, and (3) rigorously validate the model's transferabilit...

Predicting Intracranial Aneurysm Rupture: A Multifactor Analysis Combining Radscore, Morphology, and PHASES Parameters.

Academic radiology
RATIONALE AND OBJECTIVES: We aimed at developing and validating a nomogram and machine learning (ML) models based on radiomics score (Radscore), morphology, and PHASES to predict intracranial aneurysm (IA) rupture.

Revolutionizing Aneurysm detection: The role of artificial intelligence in reducing rupture rates.

Neurosurgical review
Cerebral aneurysms, affecting 2-5% of the global population, are often asymptomatic and commonly located within the Circle of Willis. A recent study in Neurosurgical Review highlights a significant reduction in the annual rupture rates of unruptured ...

Advancements in opportunistic intracranial aneurysm screening: The impact of a deep learning algorithm on radiologists' analysis of T2-weighted cranial MRI.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
(1) Background: Unruptured Intracranial Aneurysms (UIAs) are common blood vessel malformations, occurring in up to 3 % of healthy adults. Magnetic Resonance Angiography (MRA) is frequently used for the screening of UIAs due to its high resolution in ...

Enhancing Radiologists' Performance in Detecting Cerebral Aneurysms Using a Deep Learning Model: A Multicenter Study.

Academic radiology
RATIONALE AND OBJECTIVES: This study aimed to develop a deep learning (DL)-based model for detecting and diagnosing cerebral aneurysms in clinical settings, with and without human assistance.