scholarly journals Expediting Finite Element Analyses for Subject-Specific Studies of Knee Osteoarthritis: A Literature Review

2021 ◽  
Vol 11 (23) ◽  
pp. 11440
Author(s):  
Alexander Paz ◽  
Gustavo A. Orozco ◽  
Rami K. Korhonen ◽  
José J. García ◽  
Mika E. Mononen
Keyword(s):  
Finite Element ◽  
Machine Learning Techniques ◽  
Clinical Environment ◽  
Element Analysis ◽  
Knee Oa ◽  
Current Trends ◽  
Learning Techniques ◽  
Clinical Methods ◽  
Subject Specific ◽  
Daily Physical Activities

Osteoarthritis (OA) is a degenerative disease that affects the synovial joints, especially the knee joint, diminishing the ability of patients to perform daily physical activities. Unfortunately, there is no cure for this nearly irreversible musculoskeletal disorder. Nowadays, many researchers aim for in silico-based methods to simulate personalized risks for the onset and progression of OA and evaluate the effects of different conservative preventative actions. Finite element analysis (FEA) has been considered a promising method to be developed for knee OA management. The FEA pipeline consists of three well-established phases: pre-processing, processing, and post-processing. Currently, these phases are time-consuming, making the FEA workflow cumbersome for the clinical environment. Hence, in this narrative review, we overviewed present-day trends towards clinical methods for subject-specific knee OA studies utilizing FEA. We reviewed studies focused on understanding mechanisms that initiate knee OA and expediting the FEA workflow applied to the whole-organ level. Based on the current trends we observed, we believe that forthcoming knee FEAs will provide nearly real-time predictions for the personalized risk of developing knee OA. These analyses will integrate subject-specific geometries, loading conditions, and estimations of local tissue mechanical properties. This will be achieved by combining state-of-the-art FEA workflows with automated approaches aided by machine learning techniques.

Subject-specific finite element analysis of a lumbar cage produced by electron beam melting

2019 ◽  
Vol 57 (12) ◽  
pp. 2771-2781
Author(s):  
Gabriella Epasto ◽  
Fabio Distefano ◽  
Rosalia Mineo ◽  
Eugenio Guglielmino
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Electron Beam ◽  
Electron Beam Melting ◽  
Element Analysis ◽  
Subject Specific

Effect of orthopedic implants on canine long bone compression stiffness: a combined experimental and computational approach

2019 ◽  
Vol 234 (3) ◽  
pp. 255-264
Author(s):  
Cédric P Laurent ◽  
Béatrice Böhme ◽  
Jolanthe Verwaerde ◽  
Luc Papeleux ◽  
Jean-Philippe Ponthot ◽  
...  
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Surgical Techniques ◽  
Orthopedic Implants ◽  
Automatic Generation ◽  
Long Bone ◽  
Stiffness Ratio ◽  
Element Analysis ◽  
Biomechanical Behavior ◽  
Subject Specific

Osteosynthesis for canine long bones is a complex process requiring knowledge of biology, surgical techniques and (bio)mechanical principles. Subject-specific finite element analysis constitutes a promising tool to evaluate the effect of surgical intervention on the global properties of a bone–implant construct, but suffers from a lack of validation. In this study, the biomechanical behavior of 10 canine humeri was compared before and after creation of a 10 mm bone defect stabilized with an eight-hole locking compression plate (Synthes®) and two locking screws on each fragment. The response under compression of both intact and plated samples was measured experimentally and reproduced with a finite element model. The experimental stiffness ratio between plated and intact bone was equal to 0.39 ± 0.06. A subject-specific finite element analysis including density-dependent elasto-plastic material properties for canine bone and automatic generation of orthopedic implants was then conducted to recover these experimental results. The stiffness of intact and plated samples could be predicted, with no significant differences with experimental data. The simulated stiffness ratio between plated and intact canine bone was equal to 0.43 ± 0.03. This study constitutes a first step toward the building of a virtual database of pre-computed cases, aiming at helping the veterinary surgeons to make decisions regarding the most suited orthopedic solution for a given dog and a given fracture.

Investigating Machine Learning Techniques for User Sentiment Analysis

2019 ◽  
Vol 11 (3) ◽  
pp. 1-12 ◽  
Author(s):  
Nimesh V Patel ◽  
Hitesh Chhinkaniwala
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Product Reviews ◽  
Learning Approaches ◽  
Current Trends ◽  
Learning Techniques ◽  
Benchmark Datasets

Sentiment analysis identifies users in the textual reviews available in social networking sites, tweets, blog posts, forums, status updates to share their emotions or reviews and these reviews are to be used by market researchers to do know the product reviews and current trends in the market. The sentiment analysis is performed by two methods. Machine learning approaches and lexicon methods which are also known as the knowledge base approach. These. In this article, the authors evaluate the performance of some machine learning techniques: Maximum Entropy, Naïve Bayes and Support Vector Machines on two benchmark datasets: the positive-negative dataset and a Movie Review dataset by measuring parameters like accuracy, precision, recall and F-score. In this article, the authors present the performance of various sentiment analysis and classification methods by classifying the reviews in binary classes as positive, negative opinion about reviews on different domains of dataset. It is also justified that sentiment analysis using the Support Vector Machine outperforms other machine learning techniques.

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