scholarly journals HoloYolo: A proof‐of‐concept study for marker‐less surgical navigation of spinal rod implants with augmented reality and on‐device machine learning

2020 ◽  
Author(s):  
Marco von Atzigen ◽  
Florentin Liebmann ◽  
Armando Hoch ◽  
David E. Bauer ◽  
Jess Gerrit Snedeker ◽  
...  
Keyword(s):  
Machine Learning ◽  
Augmented Reality ◽  
Surgical Navigation ◽  
Proof Of Concept ◽  
Concept Study

scholarly journals Augmented reality fluoroscopy simulation of the guide-wire insertion in DHS surgery: A proof of concept study

2018 ◽  
Vol 55 ◽  
pp. 52-59 ◽  
Author(s):  
B.H. van Duren ◽  
K. Sugand ◽  
R. Wescott ◽  
R. Carrington ◽  
A. Hart
Keyword(s):  
Augmented Reality ◽  
Guide Wire ◽  
Proof Of Concept ◽  
Concept Study

Supporting fibula free flap harvest with augmented reality: A proof‐of‐concept study

2019 ◽  
Vol 130 (5) ◽  
pp. 1173-1179
Author(s):  
Piotr Pietruski ◽  
Marcin Majak ◽  
Ewelina Świątek‐Najwer ◽  
Magdalena Żuk ◽  
Michał Popek ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Free Flap ◽  
Proof Of Concept ◽  
Concept Study ◽  
Fibula Free Flap

scholarly journals A Proof of Concept Study of Using Machine-Learning in Artificial Aortic Valve Design: From Leaflet Design to Stress Analysis

2019 ◽  
Vol 6 (4) ◽  
pp. 104 ◽  
Author(s):  
Liang Liang ◽  
Bill Sun
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Aortic Valve ◽  
Stress Analysis ◽  
Heart Valves ◽  
Deep Neural Networks ◽  
Design Parameters ◽  
Attractive Alternative ◽  
Proof Of Concept ◽  
Concept Study

Artificial heart valves, used to replace diseased human heart valves, are life-saving medical devices. Currently, at the device development stage, new artificial valves are primarily assessed through time-consuming and expensive benchtop tests or animal implantation studies. Computational stress analysis using the finite element (FE) method presents an attractive alternative to physical testing. However, FE computational analysis requires a complex process of numeric modeling and simulation, as well as in-depth engineering expertise. In this proof of concept study, our objective was to develop machine learning (ML) techniques that can estimate the stress and deformation of a transcatheter aortic valve (TAV) from a given set of TAV leaflet design parameters. Two deep neural networks were developed and compared: the autoencoder-based ML-models and the direct ML-models. The ML-models were evaluated through Monte Carlo cross validation. From the results, both proposed deep neural networks could accurately estimate the deformed geometry of the TAV leaflets and the associated stress distributions within a second, with the direct ML-models (ML-model-d) having slightly larger errors. In conclusion, although this is a proof-of-concept study, the proposed ML approaches have demonstrated great potential to serve as a fast and reliable tool for future TAV design.

scholarly journals A Novel Machine Learning Sepsis Prediction Algorithm for Intended ICU Use (NAVOY® Sepsis): A Proof-of-Concept Study (Preprint)

10.2196/28000 ◽  
2021 ◽  
Author(s):  
Inger Persson ◽  
Andreas Östling ◽  
Martin Arlbrandt ◽  
Joakim Söderberg ◽  
David Becedas
Keyword(s):  
Machine Learning ◽  
Prediction Algorithm ◽  
Proof Of Concept ◽  
Concept Study
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