Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Bastian Oliver Sabel"'
Autor:
Bernd Erber, Vincent Schwarze, Frederik Strobl, Alexander Burges, Sven Mahner, Sophia Samira Goller, Jan Rudolph, Jens Ricke, Bastian Oliver Sabel
Publikováno v:
Healthcare, Vol 10, Iss 8, p 1471 (2022)
MR-guided high-intensity focused ultrasound (MR-HIFU) is an effective method for treating symptomatic uterine fibroids, especially solitary lesions. The aim of our study was to compare the clinical and morphological outcomes of patients who underwent
Externí odkaz:
https://doaj.org/article/d03fefa0dca34936809b3a49c1b742c1
Autor:
Jan Rudolph, Nicola Fink, Julien Dinkel, Vanessa Koliogiannis, Vincent Schwarze, Sophia Goller, Bernd Erber, Thomas Geyer, Boj Friedrich Hoppe, Maximilian Fischer, Najib Ben Khaled, Maximilian Jörgens, Jens Ricke, Johannes Rueckel, Bastian Oliver Sabel
Publikováno v:
Diagnostics, Vol 11, Iss 10, p 1868 (2021)
(1) Background: Chest radiography (CXR) is still a key diagnostic component in the emergency department (ED). Correct interpretation is essential since some pathologies require urgent treatment. This study quantifies potential discrepancies in CXR an
Externí odkaz:
https://doaj.org/article/238c7318807042fe98a70f828486a3d6
Autor:
Vincent Schwarze, Johannes Rübenthaler, Saša Čečatka, Constantin Marschner, Matthias Frank Froelich, Bastian Oliver Sabel, Michael Staehler, Thomas Knösel, Thomas Geyer, Dirk-André Clevert
Publikováno v:
Medicina, Vol 56, Iss 12, p 692 (2020)
Background and objectives: The aim of the present retrospective single-center study is to evaluate the diagnostic performance of contrast-enhanced ultrasound (CEUS) for assessing Bosniak III complex renal cystic lesions with histopathological validat
Externí odkaz:
https://doaj.org/article/e86d26ecbd49490f83a8431464d4ae3d
Autor:
Philipp Wesp, Bastian Oliver Sabel, Andreas Mittermeier, Anna Theresa Stüber, Katharina Jeblick, Patrick Schinke, Marc Mühlmann, Florian Fischer, Randolph Penning, Jens Ricke, Michael Ingrisch, Balthasar Maria Schachtner
Publikováno v:
International Journal of Legal Medicine. 137:733-742
Background Deep learning is a promising technique to improve radiological age assessment. However, expensive manual annotation by experts poses a bottleneck for creating large datasets to appropriately train deep neural networks. We propose an object
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::55c130f6ee7a85718f8cd05256b76a92
https://doi.org/10.1007/s00414-023-02958-7
https://doi.org/10.1007/s00414-023-02958-7
Autor:
Fatemeh Homayounieh, Subba Digumarthy, Shadi Ebrahimian, Johannes Rueckel, Boj Friedrich Hoppe, Bastian Oliver Sabel, Sailesh Conjeti, Karsten Ridder, Markus Sistermanns, Lei Wang, Alexander Preuhs, Florin Ghesu, Awais Mansoor, Mateen Moghbel, Ariel Botwin, Ramandeep Singh, Samuel Cartmell, John Patti, Christian Huemmer, Andreas Fieselmann, Clemens Joerger, Negar Mirshahzadeh, Victorine Muse, Mannudeep Kalra
Publikováno v:
JAMA Network Open
Key Points Question Can artificial intelligence (AI) improve detection of pulmonary nodules on chest radiographs at different levels of detection difficulty? Findings In this diagnostic study, AI-aided interpretation was associated with significantly
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::89595635ddf6c5207ecbc7a01a0132de
http://europepmc.org/articles/PMC8717119
http://europepmc.org/articles/PMC8717119