scholarly journals Detecting total hip replacement prosthesis design on plain radiographs using deep convolutional neural network

2020 ◽  
Vol 38 (7) ◽  
pp. 1465-1471 ◽  
Author(s):  
Alireza Borjali ◽  
Antonia F. Chen ◽  
Orhun K. Muratoglu ◽  
Mohammad A. Morid ◽  
Kartik M. Varadarajan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Total Hip Replacement ◽  
Hip Replacement ◽  
Deep Convolutional Neural Network ◽  
Prosthesis Design ◽  
Plain Radiographs ◽  
Total Hip ◽  
Replacement Prosthesis

scholarly journals Comparing performance of deep convolutional neural network with orthopaedic surgeons on identification of total hip prosthesis design from plain radiographs

2020 ◽  
Author(s):  
Alireza Borjali ◽  
Antonia F. Chen ◽  
Hany S. Bedair ◽  
Christopher M. Melnic ◽  
Orhun K. Muratoglu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Training Data ◽  
Identification Accuracy ◽  
Automated System ◽  
Implant Design ◽  
Plain Radiographs ◽  
Orthopaedic Surgeons ◽  
Total Hip

ABSTRACTA crucial step in preoperative planning for a revision total hip replacement (THR) surgery is accurate identification of failed implant design, especially if one or more well-fixed/functioning components are to be retained. Manual identification of the implant design from preoperative radiographic images can be time-consuming and inaccurate, which can ultimately lead to increased operating room time, more complex surgery, and increased healthcare costs. No automated system has been developed to accurately and efficiently identify THR implant designs. In this study, we present a novel, fully automatic and interpretable approach to identify the design of nine different THR femoral implants from plain radiographs using deep convolutional neural network (CNN). We also compared the CNN’s performance with three board-certified and fellowship trained orthopaedic surgeons. The CNN achieved on-par accuracy with the orthopaedic surgeons while being significantly faster. The need for additional training data for less distinct designs was also highlighted. Such CNN can be used to automatically identify the design of a failed THR femoral implant preoperatively in just a fraction of a second, saving time and improving identification accuracy.

scholarly journals Early failure of the Ultima metal-on-metal total hip replacement in the presence of normal plain radiographs

2010 ◽  
Vol 92-B (11) ◽  
pp. 1501-1508 ◽  
Author(s):  
S. T. Donell ◽  
C. Darrah ◽  
J. F. Nolan ◽  
J. Wimhurst ◽  
A. Toms ◽  
...  
Keyword(s):  
Total Hip Replacement ◽  
Hip Replacement ◽  
Early Failure ◽  
Plain Radiographs ◽  
Total Hip ◽  
Metal On Metal

scholarly journals Acid Attack and Cathepsin K in Bone Resorption Around Total Hip Replacement Prosthesis

2001 ◽  
Vol 16 (10) ◽  
pp. 1780-1786 ◽  
Author(s):  
Yrjö T. Konttinen ◽  
Michiaki Takagi ◽  
Jami Mandelin ◽  
Jan Lassus ◽  
Jari Salo ◽  
...  
Keyword(s):  
Bone Resorption ◽  
Total Hip Replacement ◽  
Hip Replacement ◽  
Cathepsin K ◽  
Acid Attack ◽  
Total Hip ◽  
Replacement Prosthesis
Sign in / Sign up

Export Citation Format

Share Document