Precision study of DXA-based patient-specific finite element modeling for assessing hip fracture risk

2013 ◽  
Vol 29 (5) ◽  
pp. 615-629 ◽  
Author(s):  
Yunhua Luo ◽  
Zannatul Ferdous ◽  
William D. Leslie
Keyword(s):  
Finite Element ◽  
Hip Fracture ◽  
Fracture Risk ◽  
Finite Element Modeling ◽  
Patient Specific ◽  
Element Modeling ◽  
Hip Fracture Risk ◽  
Precision Study

scholarly journals Patient-specific Hip Fracture Strength Assessment with Microstructural MR Imaging–based Finite Element Modeling

Radiology ◽  
2017 ◽  
Vol 283 (3) ◽  
pp. 854-861 ◽  
Author(s):  
Chamith S. Rajapakse ◽  
Alexandra Hotca ◽  
Benjamin T. Newman ◽  
Austin Ramme ◽  
Shaleen Vira ◽  
...  
Keyword(s):  
Finite Element ◽  
Hip Fracture ◽  
Mr Imaging ◽  
Finite Element Modeling ◽  
Fracture Strength ◽  
Patient Specific ◽  
Strength Assessment ◽  
Element Modeling

scholarly journals Method for patient-specific finite element modeling and simulation of deep brain stimulation

2008 ◽  
Vol 47 (1) ◽  
pp. 21-28 ◽  
Author(s):  
Mattias Åström ◽  
Ludvic U. Zrinzo ◽  
Stephen Tisch ◽  
Elina Tripoliti ◽  
Marwan I. Hariz ◽  
...  
Keyword(s):  
Finite Element ◽  
Deep Brain Stimulation ◽  
Finite Element Modeling ◽  
Modeling And Simulation ◽  
Brain Stimulation ◽  
Patient Specific ◽  
Element Modeling ◽  
Deep Brain

Finite Element Modeling of the Influence of Hand Position and Bone Properties on the Colles’ Fracture Load During a Fall

2010 ◽  
Vol 132 (8) ◽  
Author(s):  
Drew Buchanan ◽  
Ani Ural
Keyword(s):  
Finite Element ◽  
Fracture Risk ◽  
Finite Element Modeling ◽  
Bone Geometry ◽  
Load Ratio ◽  
Fracture Load ◽  
Extrinsic Factors ◽  
Element Modeling ◽  
Bone Properties ◽  
Colles Fracture

Distal forearm fracture is one of the most frequently observed osteoporotic fractures, which may occur as a result of low energy falls such as falls from a standing height and may be linked to the osteoporotic nature of the bone, especially in the elderly. In order to prevent the occurrence of radius fractures and their adverse outcomes, understanding the effect of both extrinsic and intrinsic contributors to fracture risk is essential. In this study, a nonlinear fracture mechanics-based finite element model is applied to human radius to assess the influence of extrinsic factors (load orientation and load distribution between scaphoid and lunate) and intrinsic bone properties (age-related changes in fracture properties and bone geometry) on the Colles’ fracture load. Seven three-dimensional finite element models of radius were created, and the fracture loads were determined by using cohesive finite element modeling, which explicitly represented the crack and the fracture process zone behavior. The simulation results showed that the load direction with respect to the longitudinal and dorsal axes of the radius influenced the fracture load. The fracture load increased with larger angles between the resultant load and the dorsal axis, and with smaller angles between the resultant load and longitudinal axis. The fracture load also varied as a function of the load ratio between the lunate and scaphoid, however, not as drastically as with the load orientation. The fracture load decreased as the load ratio (lunate/scaphoid) increased. Multiple regression analysis showed that the bone geometry and the load orientation are the most important variables that contribute to the prediction of the fracture load. The findings in this study establish a robust computational fracture risk assessment method that combines the effects of intrinsic properties of bone with extrinsic factors associated with a fall, and may be elemental in the identification of high fracture risk individuals as well as in the development of fracture prevention methods including protective falling techniques. The additional information that this study brings to fracture identification and prevention highlights the promise of fracture mechanics-based finite element modeling in fracture risk assessment.

Finite Element Analysis of Proximal Femur QCT Scans for the Assessment of Hip Fracture Risk In Older Men

2008 ◽  
Vol 11 (3) ◽  
pp. 467-468 ◽  
Author(s):  
Tony M. Keaveny ◽  
Lynn M. Marshall ◽  
Carrie M. Nielson ◽  
Steven R. Cummings ◽  
Paul F. Hoffmann ◽  
...  
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Hip Fracture ◽  
Fracture Risk ◽  
Proximal Femur ◽  
Older Men ◽  
Element Analysis ◽  
Hip Fracture Risk

A Predictive Analysis of Wall Stress in Abdominal Aortic Aneurysms Using a Neural Network Model

2021 ◽  
Author(s):  
Balaji Rengarajan ◽  
Sourav Patnaik ◽  
Ender A. Finol
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Finite Element Modeling ◽  
Wall Stress ◽  
Aortic Aneurysms ◽  
Multivariate Adaptive Regression Splines ◽  
Patient Specific ◽  
Clinical Images ◽  
Element Modeling ◽  
Abdominal Aortic

Abstract In the present work, we investigated the use of geometric indices to predict patient-specific abdominal aortic aneurysm (AAA) wall stress by means of a novel neural network (NN) modeling approach. We conducted a retrospective review of existing clinical images of two patient groups: 98 asymptomatic and 50 symptomatic AAA. The images were subject to a protocol consisting of image segmentation, processing, volume meshing, finite element modeling, and geometry quantification, from which 53 geometric indices and the spatially averaged wall stress (SAWS) were calculated. We developed feed-forward NN models composed of an input layer, two dense layers, and an output layer using Keras, a deep learning library in Python. The NN models were trained, tested, and validated independently for both AAA groups using all geometric indices, as well as a reduced set of indices resulting from a variable reduction procedure. We compared the performance of the NN models with two standard machine learning algorithms (MARS: multivariate adaptive regression splines and GAM: generalized additive model) and a linear regression model (GLM: generalized linear model). The NN-based approach exhibited the highest overall mean goodness-of-fit and lowest overall relative error compared to MARS, GAM, and GLM, when using the reduced sets of indices to predict SAWS for both AAA groups. The use of NN modeling represents a promising alternative methodology for the estimation of AAA wall stress using geometric indices as surrogates, in lieu of finite element modeling.

Sign in / Sign up

Export Citation Format

Share Document