Multicentric development and validation of a multi-scale and multi-task deep learning model for comprehensive lower extremity alignment analysis
Nikolas Wilhelm(American Association of Orthodontists), Marco‐Christopher Rupp(TUM Klinikum), Jan Neumann(TUM Klinikum), Rüdiger von Eisenhart‐Rothe(TUM Klinikum), Yannick J. Ehmann(TUM Klinikum), Maximilian Frederik Russe(University Medical Center Freiburg), Felix Lindner, Matthias J. Feucht(University of Freiburg), Sebastian Siebenlist(Steadman Philippon Research Institute), Rainer Burgkart(TUM Klinikum), Claudio E. von Schacky(TUM Klinikum), Kaywan Izadpanah(Charité - Universitätsmedizin Berlin), Florian Hinterwimmer(TUM Klinikum), Jonas Pogorzelski(Technical University of Munich), Matthias Jung(University of Freiburg), Sami Haddadin(Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR))
Cited by 14
Related Papers
Evaluation of 3D blood flow patterns and wall shear stress in the normal and dilated thoracic aorta using flow-sensitive 4D CMR
|Journal of Cardiovascular Magnetic Resonance|2012|233
Three‐dimensional analysis of segmental wall shear stress in the aorta by flow‐sensitive four‐dimensional‐MRI
|Journal of Magnetic Resonance Imaging|2009|178
Multitask Deep Learning for Segmentation and Classification of Primary Bone Tumors on Radiographs
|Radiology|2021|134
The Impact of Osseous Malalignment and Realignment Procedures in Knee Ligament Surgery: A Systematic Review of the Clinical Evidence
|Orthopaedic Journal of Sports Medicine|2017|108
Control of Posterior Tibial Slope and Patellar Height in Open-Wedge Valgus High Tibial Osteotomy
|The American Journal of Sports Medicine|2011|90