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Dataset results
12 results for “Circle of Willis;”
Circle of Willis Model and Time Resolved Flows/Pressures of In Vitro Setup
<p>Circle of Willis.stl: An STL file of the Circle of Willis model used in the publication by Luisi et al. 2022</p> <p>Physiological Flow_Scenario A.mat: Time-resolved flows and pressures at all vessel inlets and outlets from the in vitro setup in the physiological scenario.</p>
Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Complete CoW)
<p>This repository contains the dataset for the complete CoW described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).</p>
Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Missing ACoA)
<p>This repository contains the dataset for the Missing ACoA described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).</p>
Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database:Missing PCoA)
<p>This repository contains the dataset for the Missing PCoA described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).</p>
Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Missing PCoA and PCA P1)
<p>This repository contains the dataset for the Missing PCoA and PCA P1 described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).</p>
Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Missing ACA A1)
<p>This repository contains the dataset for the Missing ACA A1 described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).</p>
Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Missing PCoAs)
<p>This repository contains the dataset for the Missing PCoAs described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).</p>
Intracranial aneurysm associated single-nucleotide polymorphisms alter regulatory DNA in the human circle of Willis
GEO Series GSE107196. Homo sapiens. 14 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Missing PCA P1)
<p>This repository contains the dataset for the Missing PCA P1 described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).</p>
Circle of Willis Variants in Nepali Population Evaluated in TOF MR Angiography
ClinicalTrials.gov study NCT06751420. IPD Sharing: NO. Countries: 1. Publications: 0.
RNA sequencing analysis of rupture-prone intracranial aneurysms and the remaining circle of Willis in rats
GEO Series GSE161044. Rattus norvegicus. 6 samples. Type: Expression profiling by high throughput sequencing.
Data set from Varga A, Di Leo G, Banga PV, Csobay-Novák C, Kolossváry M, Maurovich-Horvat P, Hüttl K. Multidetector CT angiography of the Circle of Willis: association of its variants with carotid artery disease and brain ischemia. Eur Radiol. 2019 Jan;29(1):46-56. doi: 10.1007/s00330-018-5577-x. Epub 2018 Jun 19. PMID: 29922933; PMCID: PMC6291432.
<p>Data set from the article Data set from Varga A, Di Leo G, Banga PV, Csobay-Novák C, Kolossváry M, Maurovich-Horvat P, Hüttl K. Multidetector CT angiography of the Circle of Willis: association of its variants with carotid artery disease and brain ischemia. Eur Radiol. 2019 Jan;29(1):46-56. doi: 10.1007/s00330-018-5577-x. Epub 2018 Jun 19. PMID: 29922933; PMCID: PMC6291432.</p> <p>This is the abstract</p> <p><strong>Purpose: </strong> (1) to estimate the prevalence of Circle of Willis (CoW) variants in patients undergoing carotid endarterectomy, (2) to correlate these variants to controls and (3) cerebral ischemia depicted by computed tomography (CT).</p> <p><strong>Materials and methods: </strong> After Institutional Review Board approval, data of 544 carotid endarterectomy patients (331 males, mean age 69±8 years) and 196 controls (117 males, mean age 66±11 years) who underwent brain CT and carotid CT angiography (CTA) were retrospectively analysed. Two observers independently classified each CoW segment as normal, hypoplastic (diameter <0.8 mm) or non-visualized. Four groups of CoW variants based on the number of hypoplastic/non-visualized segments were correlated with clinical data (ANOVA, χ<sup>2</sup> and multivariate logistic regression analysis). Intra- and inter-observer agreement was estimated using Cohen κ statistics.</p> <p><strong>Results: </strong> High prevalence of CoW variants (97%) and compromised CoW (81%) was observed in the study group and significant difference was found in the distribution of CoW variants compared to controls (p<0.001), internal carotid artery (ICA) stenosis being the only independent predictor of CoW morphology (p<0.001). Significant correlation was found between CoW configuration and brain ischemia in the study group (p=0.002). ICA stenosis of ≥90% was associated to higher rate of ipsilateral A1 hypoplasia/non-visualization (p<0.001). Intra- and inter-observer agreement was from substantial to almost perfect (Cohen κ=0.75-1.0).</p> <p><strong>Conclusion: </strong> Highly variable CoW morphology was demonstrated in patients undergoing endarterectomy compared to controls. Likely compromised CoW in relation to cerebral ischemia was observed in a large cohort of carotid endarterectomy subjects.</p> <p><strong>Key points: </strong> • CoW variant distribution significantly differed in the study and control groups (p<0.001). • ICA stenosis was the only independent predictor of CoW morphology (p<0.001). • Severely compromised CoW configuration showed significant association with brain ischemia (p=0.002).</p>
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