Mitral valve repair with minimally invasive approaches vs sternotomy: A meta‐analysis of early and late results in randomized and matched observational studies

2020 ◽  
Vol 35 (9) ◽  
pp. 2307-2323
Author(s):  
Michel Pompeu B. O. Sá ◽  
Jef Van den Eynde ◽  
Luiz Rafael P. Cavalcanti ◽  
Bakytbek Kadyraliev ◽  
Soslan Enginoev ◽  
...  



2021 ◽  
Vol 16 (16) ◽  
pp. 1359-1365
Author(s):  
Alexander Jobs ◽  
Simon Grund ◽  
Suzanne de Waha-Thiele ◽  
Jakob Ledwoch ◽  
Horst Sievert ◽  
...  


1994 ◽  
Vol 107 (5) ◽  
pp. 1262-1271 ◽  
Author(s):  
Alon S. Aharon ◽  
Hillel Laks ◽  
Davis C. Drinkwater ◽  
Reema Chugh ◽  
Richard N. Gates ◽  
...  






2020 ◽  
Author(s):  
H. Schaefer ◽  
D. Krankenberg ◽  
U. Walle ◽  
M. Liebrich ◽  
D. Roser ◽  
...  


2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Matthias Ivantsits ◽  
Lennart Tautz ◽  
Simon Sündermann ◽  
Isaac Wamala ◽  
Jörg Kempfert ◽  
...  

AbstractMinimally invasive surgery is increasingly utilized for mitral valve repair and replacement. The intervention is performed with an endoscopic field of view on the arrested heart. Extracting the necessary information from the live endoscopic video stream is challenging due to the moving camera position, the high variability of defects, and occlusion of structures by instruments. During such minimally invasive interventions there is no time to segment regions of interest manually. We propose a real-time-capable deep-learning-based approach to detect and segment the relevant anatomical structures and instruments. For the universal deployment of the proposed solution, we evaluate them on pixel accuracy as well as distance measurements of the detected contours. The U-Net, Google’s DeepLab v3, and the Obelisk-Net models are cross-validated, with DeepLab showing superior results in pixel accuracy and distance measurements.



Author(s):  
Tikal Kansara ◽  
Ashish Kumar ◽  
Monil Majmundar ◽  
Craig Basman


Author(s):  
Ilias P. Doulamis ◽  
Aspasia Tzani ◽  
Polydoros N. Kampaktsis ◽  
Tsuyoshi Kaneko ◽  
Gilbert H.L. Tang


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