The Experts below are selected from a list of 16437 Experts worldwide ranked by ideXlab platform
Kartik M Varadarajan - One of the best experts on this subject based on the ideXlab platform.
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detecting total hip replacement Prosthesis Design on plain radiographs using deep convolutional neural network
Journal of Orthopaedic Research, 2020Co-Authors: Alireza Borjali, Antonia F Chen, Orhun K Muratoglu, Mohammad Amin Morid, Kartik M VaradarajanAbstract:Identifying the Design of a failed implant is a key step in the preoperative planning of revision total joint arthroplasty. Manual identification of the implant Design from radiographic images is time-consuming and prone to error. Failure to identify the implant Design preoperatively can lead to increased operating room time, more complex surgery, increased blood loss, increased bone loss, increased recovery time, and overall increased healthcare costs. In this study, we present a novel, fully automatic and interpretable approach to identify the Design of total hip replacement (THR) implants from plain radiographs using deep convolutional neural network (CNN). CNN achieved 100% accuracy in the identification of three commonly used THR implant Designs. Such CNN can be used to automatically identify the Design of a failed THR implant preoperatively in just a few seconds, saving time and improving the identification accuracy. This can potentially improve patient outcomes, free practitioners' time, and reduce healthcare costs.
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detecting total hip replacement Prosthesis Design on plain radiographs using deep convolutional neural network
Journal of Orthopaedic Research, 2020Co-Authors: Alireza Borjali, Antonia F Chen, Orhun K Muratoglu, Mohammad Amin Morid, Kartik M VaradarajanAbstract:: Identifying the Design of a failed implant is a key step in preoperative planning of revision total joint arthroplasty. Manual identification of the implant Design from radiographic images is time consuming and prone to error. Failure to identify the implant Design preoperatively can lead to increased operating room time, more complex surgery, increased blood loss, increased bone loss, increased recovery time, and overall increased healthcare costs. In this study, we present a novel, fully automatic and interpretable approach to identify the Design of total hip replacement (THR) implants from plain radiographs using deep convolutional neural network (CNN). CNN achieved 100% accuracy in identification of three commonly used THR implant Designs. Such CNN can be used to automatically identify the Design of a failed THR implant preoperatively in just a few seconds, saving time and improving the identification accuracy. This can potentially improve patient outcomes, free practitioners time, and reduce healthcare costs. This article is protected by copyright. All rights reserved.
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detecting total hip replacement Prosthesis Design on preoperative radiographs using deep convolutional neural network
arXiv: Image and Video Processing, 2019Co-Authors: Alireza Borjali, Antonia F Chen, Orhun K Muratoglu, Mohammad Amin Morid, Kartik M VaradarajanAbstract:Identifying the Design of a failed implant is a key step in preoperative planning of revision total joint arthroplasty. Manual identification of the implant Design from radiographic images is time consuming and prone to error. Failure to identify the implant Design preoperatively can lead to increased operating room time, more complex surgery, increased blood loss, increased bone loss, increased recovery time, and overall increased healthcare costs. In this study, we present a novel, fully automatic and interpretable approach to identify the Design of total hip replacement (THR) implants from plain radiographs using deep convolutional neural network (CNN). CNN achieved 100% accuracy in identification of three commonly used THR implant Designs. Such CNN can be used to automatically identify the Design of a failed THR implant preoperatively in just a few seconds, saving time and improving the identification accuracy. This can potentially improve patient outcomes, free practitioners time, and reduce healthcare costs.
Alireza Borjali - One of the best experts on this subject based on the ideXlab platform.
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detecting total hip replacement Prosthesis Design on plain radiographs using deep convolutional neural network
Journal of Orthopaedic Research, 2020Co-Authors: Alireza Borjali, Antonia F Chen, Orhun K Muratoglu, Mohammad Amin Morid, Kartik M VaradarajanAbstract:Identifying the Design of a failed implant is a key step in the preoperative planning of revision total joint arthroplasty. Manual identification of the implant Design from radiographic images is time-consuming and prone to error. Failure to identify the implant Design preoperatively can lead to increased operating room time, more complex surgery, increased blood loss, increased bone loss, increased recovery time, and overall increased healthcare costs. In this study, we present a novel, fully automatic and interpretable approach to identify the Design of total hip replacement (THR) implants from plain radiographs using deep convolutional neural network (CNN). CNN achieved 100% accuracy in the identification of three commonly used THR implant Designs. Such CNN can be used to automatically identify the Design of a failed THR implant preoperatively in just a few seconds, saving time and improving the identification accuracy. This can potentially improve patient outcomes, free practitioners' time, and reduce healthcare costs.
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detecting total hip replacement Prosthesis Design on plain radiographs using deep convolutional neural network
Journal of Orthopaedic Research, 2020Co-Authors: Alireza Borjali, Antonia F Chen, Orhun K Muratoglu, Mohammad Amin Morid, Kartik M VaradarajanAbstract:: Identifying the Design of a failed implant is a key step in preoperative planning of revision total joint arthroplasty. Manual identification of the implant Design from radiographic images is time consuming and prone to error. Failure to identify the implant Design preoperatively can lead to increased operating room time, more complex surgery, increased blood loss, increased bone loss, increased recovery time, and overall increased healthcare costs. In this study, we present a novel, fully automatic and interpretable approach to identify the Design of total hip replacement (THR) implants from plain radiographs using deep convolutional neural network (CNN). CNN achieved 100% accuracy in identification of three commonly used THR implant Designs. Such CNN can be used to automatically identify the Design of a failed THR implant preoperatively in just a few seconds, saving time and improving the identification accuracy. This can potentially improve patient outcomes, free practitioners time, and reduce healthcare costs. This article is protected by copyright. All rights reserved.
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detecting total hip replacement Prosthesis Design on preoperative radiographs using deep convolutional neural network
arXiv: Image and Video Processing, 2019Co-Authors: Alireza Borjali, Antonia F Chen, Orhun K Muratoglu, Mohammad Amin Morid, Kartik M VaradarajanAbstract:Identifying the Design of a failed implant is a key step in preoperative planning of revision total joint arthroplasty. Manual identification of the implant Design from radiographic images is time consuming and prone to error. Failure to identify the implant Design preoperatively can lead to increased operating room time, more complex surgery, increased blood loss, increased bone loss, increased recovery time, and overall increased healthcare costs. In this study, we present a novel, fully automatic and interpretable approach to identify the Design of total hip replacement (THR) implants from plain radiographs using deep convolutional neural network (CNN). CNN achieved 100% accuracy in identification of three commonly used THR implant Designs. Such CNN can be used to automatically identify the Design of a failed THR implant preoperatively in just a few seconds, saving time and improving the identification accuracy. This can potentially improve patient outcomes, free practitioners time, and reduce healthcare costs.
Caterina Rizzi - One of the best experts on this subject based on the ideXlab platform.
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A virtual platform for lower limb Prosthesis Design and assessment
DHM and Posturography, 2019Co-Authors: Daniele Regazzoni, Andrea Vitali, Caterina Rizzi, Giorgio ColomboAbstract:Abstract This scientific work aims at developing an innovative virtual platform to Design lower limb Prosthesis centered on the virtual model of the patient and based on a computer-aided and knowledge-guided approach. The main idea is to develop a digital human model of the amputee to be used by the prosthetist in a full virtual environment in which a platform provides a set of interactive tools to Design, configure, and test the Prosthesis. This virtual platform permits to Design and configure the whole Prosthesis, in particular, the 3D model of the assembled Prosthesis, crucial to define the Prosthesis setup and patient's walking performance. An ad-hoc computer-aided Design system has been developed in house to Design the 3D model of the socket according to traditional operations made by technicians during traditional manufacturing process. Moreover, a finite element model has been defined to study the contact between residual limb and socket. The resulting 3D model of the socket can be realized by exploiting multimaterial additive manufacturing technology. Finally, the developed platform also permits to handle contact pressures and patient's gait data in a unique application through the use of a low-cost motion capture (MOCAP) system. The whole platform has been tested with the help of an Italian orthopedic laboratory. The developed platform is a promising solution to develop the check socket, and the application may be used also for training purpose for junior orthopedic technicians.
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a digital patient for computer aided Prosthesis Design
Interface Focus, 2013Co-Authors: Giorgio Colombo, Giancarlo Facoetti, Caterina RizziAbstract:This article concerns the Design of lower limb Prosthesis, both below and above knee. It describes a new computer-based Design framework and a digital model of the patient around which the Prosthesis is Designed and tested in a completely virtual environment. The virtual model of the patient is the backbone of the whole system, and it is based on a biomechanical general-purpose model customized with the patient9s characteristics (e.g. anthropometric measures). The software platform adopts computer-aided and knowledge-guided approaches with the goal of replacing the current development process, mainly hand made, with a virtual one. It provides the prosthetics with a set of tools to Design, configure and test the Prosthesis and comprehends two main environments: the Prosthesis modelling laboratory and the virtual testing laboratory. The first permits the three-dimensional model of the Prosthesis to be configured and generated, while the second allows the prosthetics to virtually set up the artificial leg and simulate the patient9s postures and movements, validating its functionality and configuration. General architecture and modelling/simulation tools for the platform are described as well as main aspects and results of the experimentation.
Mohammad Amin Morid - One of the best experts on this subject based on the ideXlab platform.
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detecting total hip replacement Prosthesis Design on plain radiographs using deep convolutional neural network
Journal of Orthopaedic Research, 2020Co-Authors: Alireza Borjali, Antonia F Chen, Orhun K Muratoglu, Mohammad Amin Morid, Kartik M VaradarajanAbstract:Identifying the Design of a failed implant is a key step in the preoperative planning of revision total joint arthroplasty. Manual identification of the implant Design from radiographic images is time-consuming and prone to error. Failure to identify the implant Design preoperatively can lead to increased operating room time, more complex surgery, increased blood loss, increased bone loss, increased recovery time, and overall increased healthcare costs. In this study, we present a novel, fully automatic and interpretable approach to identify the Design of total hip replacement (THR) implants from plain radiographs using deep convolutional neural network (CNN). CNN achieved 100% accuracy in the identification of three commonly used THR implant Designs. Such CNN can be used to automatically identify the Design of a failed THR implant preoperatively in just a few seconds, saving time and improving the identification accuracy. This can potentially improve patient outcomes, free practitioners' time, and reduce healthcare costs.
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detecting total hip replacement Prosthesis Design on plain radiographs using deep convolutional neural network
Journal of Orthopaedic Research, 2020Co-Authors: Alireza Borjali, Antonia F Chen, Orhun K Muratoglu, Mohammad Amin Morid, Kartik M VaradarajanAbstract:: Identifying the Design of a failed implant is a key step in preoperative planning of revision total joint arthroplasty. Manual identification of the implant Design from radiographic images is time consuming and prone to error. Failure to identify the implant Design preoperatively can lead to increased operating room time, more complex surgery, increased blood loss, increased bone loss, increased recovery time, and overall increased healthcare costs. In this study, we present a novel, fully automatic and interpretable approach to identify the Design of total hip replacement (THR) implants from plain radiographs using deep convolutional neural network (CNN). CNN achieved 100% accuracy in identification of three commonly used THR implant Designs. Such CNN can be used to automatically identify the Design of a failed THR implant preoperatively in just a few seconds, saving time and improving the identification accuracy. This can potentially improve patient outcomes, free practitioners time, and reduce healthcare costs. This article is protected by copyright. All rights reserved.
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detecting total hip replacement Prosthesis Design on preoperative radiographs using deep convolutional neural network
arXiv: Image and Video Processing, 2019Co-Authors: Alireza Borjali, Antonia F Chen, Orhun K Muratoglu, Mohammad Amin Morid, Kartik M VaradarajanAbstract:Identifying the Design of a failed implant is a key step in preoperative planning of revision total joint arthroplasty. Manual identification of the implant Design from radiographic images is time consuming and prone to error. Failure to identify the implant Design preoperatively can lead to increased operating room time, more complex surgery, increased blood loss, increased bone loss, increased recovery time, and overall increased healthcare costs. In this study, we present a novel, fully automatic and interpretable approach to identify the Design of total hip replacement (THR) implants from plain radiographs using deep convolutional neural network (CNN). CNN achieved 100% accuracy in identification of three commonly used THR implant Designs. Such CNN can be used to automatically identify the Design of a failed THR implant preoperatively in just a few seconds, saving time and improving the identification accuracy. This can potentially improve patient outcomes, free practitioners time, and reduce healthcare costs.
Antonia F Chen - One of the best experts on this subject based on the ideXlab platform.
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detecting total hip replacement Prosthesis Design on plain radiographs using deep convolutional neural network
Journal of Orthopaedic Research, 2020Co-Authors: Alireza Borjali, Antonia F Chen, Orhun K Muratoglu, Mohammad Amin Morid, Kartik M VaradarajanAbstract:Identifying the Design of a failed implant is a key step in the preoperative planning of revision total joint arthroplasty. Manual identification of the implant Design from radiographic images is time-consuming and prone to error. Failure to identify the implant Design preoperatively can lead to increased operating room time, more complex surgery, increased blood loss, increased bone loss, increased recovery time, and overall increased healthcare costs. In this study, we present a novel, fully automatic and interpretable approach to identify the Design of total hip replacement (THR) implants from plain radiographs using deep convolutional neural network (CNN). CNN achieved 100% accuracy in the identification of three commonly used THR implant Designs. Such CNN can be used to automatically identify the Design of a failed THR implant preoperatively in just a few seconds, saving time and improving the identification accuracy. This can potentially improve patient outcomes, free practitioners' time, and reduce healthcare costs.
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detecting total hip replacement Prosthesis Design on plain radiographs using deep convolutional neural network
Journal of Orthopaedic Research, 2020Co-Authors: Alireza Borjali, Antonia F Chen, Orhun K Muratoglu, Mohammad Amin Morid, Kartik M VaradarajanAbstract:: Identifying the Design of a failed implant is a key step in preoperative planning of revision total joint arthroplasty. Manual identification of the implant Design from radiographic images is time consuming and prone to error. Failure to identify the implant Design preoperatively can lead to increased operating room time, more complex surgery, increased blood loss, increased bone loss, increased recovery time, and overall increased healthcare costs. In this study, we present a novel, fully automatic and interpretable approach to identify the Design of total hip replacement (THR) implants from plain radiographs using deep convolutional neural network (CNN). CNN achieved 100% accuracy in identification of three commonly used THR implant Designs. Such CNN can be used to automatically identify the Design of a failed THR implant preoperatively in just a few seconds, saving time and improving the identification accuracy. This can potentially improve patient outcomes, free practitioners time, and reduce healthcare costs. This article is protected by copyright. All rights reserved.
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detecting total hip replacement Prosthesis Design on preoperative radiographs using deep convolutional neural network
arXiv: Image and Video Processing, 2019Co-Authors: Alireza Borjali, Antonia F Chen, Orhun K Muratoglu, Mohammad Amin Morid, Kartik M VaradarajanAbstract:Identifying the Design of a failed implant is a key step in preoperative planning of revision total joint arthroplasty. Manual identification of the implant Design from radiographic images is time consuming and prone to error. Failure to identify the implant Design preoperatively can lead to increased operating room time, more complex surgery, increased blood loss, increased bone loss, increased recovery time, and overall increased healthcare costs. In this study, we present a novel, fully automatic and interpretable approach to identify the Design of total hip replacement (THR) implants from plain radiographs using deep convolutional neural network (CNN). CNN achieved 100% accuracy in identification of three commonly used THR implant Designs. Such CNN can be used to automatically identify the Design of a failed THR implant preoperatively in just a few seconds, saving time and improving the identification accuracy. This can potentially improve patient outcomes, free practitioners time, and reduce healthcare costs.
