Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Judit Csore"'
Autor:
Hunor Sarkadi, Judit Csőre, Dániel Sándor Veres, Nándor Szegedi, Levente Molnár, László Gellér, Viktor Bérczi, Edit Dósa
Publikováno v:
PLoS ONE, Vol 16, Iss 8, p e0256317 (2021)
PurposeTo evaluate factors associated with pseudoaneurysm (PSA) development.MethodsBetween January 2016 and May 2020, 30,196 patients had invasive vascular radiological or cardiac endovascular procedures that required arterial puncture. All patients
Externí odkaz:
https://doaj.org/article/0b820f7996624fbbb7b466d4ddb3d1ea
Publikováno v:
EJVES Vascular Forum, Vol 61, Iss , Pp 121-125 (2024)
Introduction: Percutaneous deep venous arterialisation (DVA) is emerging as a promising alternative for limb salvage in chronic limb threatening ischaemia (CLTI) patients without any reasonable anatomical option for conventional revascularisation tec
Externí odkaz:
https://doaj.org/article/b4eb77d2b26243d7a40df6d3a58e5077
Publikováno v:
Journal of Vascular Surgery Cases and Innovative Techniques, Vol 9, Iss 4, Pp 101263- (2023)
With the growing prevalence and mortality of peripheral arterial disease, preoperative assessment, risk stratification, and determining the correct indication for endovascular and open surgical procedures are essential for therapeutic decision-making
Externí odkaz:
https://doaj.org/article/9019de8d8ac647359290b29bf258f068
Publikováno v:
JVS - Vascular Science, Vol 4, Iss , Pp 100148- (2023)
Externí odkaz:
https://doaj.org/article/f2b1c8bc928b4711bafdc585bee791df
Publikováno v:
Diagnostics, Vol 13, Iss 11, p 1925 (2023)
The novel approach of our study consists in adapting and in evaluating a custom-made variational autoencoder (VAE) using two-dimensional (2D) convolutional neural networks (CNNs) on magnetic resonance imaging (MRI) images for differentiate soft vs. h
Externí odkaz:
https://doaj.org/article/d5df75632cf24b359ecbaf486eaa613a
Publikováno v:
Diagnostics; Volume 13; Issue 11; Pages: 1925
The novel approach of our study consists in adapting and in evaluating a custom-made variational autoencoder (VAE) using two-dimensional (2D) convolutional neural networks (CNNs) on magnetic resonance imaging (MRI) images for differentiate soft vs. h