showing 13 related works from this author
Risk Assessment of Hip Fracture Based on Machine Learning
2020
[EN] Identifying patients with high risk of hip fracture is a great challenge in osteoporosis clinical assessment. Bone Mineral Density (BMD) measured by Dual-Energy X-Ray Absorptiometry (DXA) is the current gold standard in osteoporosis clinical assessment. However, its classification accuracy is only around 65%. In order to improve this accuracy, this paper proposes the use of Machine Learning (ML) models trained with data from a biomechanical model that simulates a sideways-fall. Machine Learning (ML) models are models able to learn and to make predictions from data. During a training process, ML models learn a function that maps inputs and outputs without previous knowledge of the probl…
Neural Networks for Modeling the Contact Foot-Shoe Upper
2010
Recently, important advances in virtual reality have made possible real improvements in computer aided design, CAD. These advances are being applied to all the fields and they have reached to the footwear design. The majority of the interaction foot-shoe simulation processes have been focused on the interaction between the foot and the sole. However, few efforts have been made in order to simulate the interaction between the shoe upper and the foot surface. To simulate this interaction, flexibility tests (characterization of the relationship between exerted force and displacement) are carried out to evaluate the materials used for the shoe upper. This chapter shows a procedure based on arti…
A finite element-based machine learning approach for modeling the mechanical behavior of the breast tissues under compression in real-time
2017
[EN] This work presents a data-driven method to simulate, in real-time, the biomechanical behavior of the breast tissues in some image-guided interventions such as biopsies or radiotherapy dose delivery as well as to speed up multimodal registration algorithms. Ten real breasts were used for this work. Their deformation due to the displacement of two compression plates was simulated off-line using the finite element (FE) method. Three machine learning models were trained with the data from those simulations. Then, they were used to predict in real-time the deformation of the breast tissues during the compression. The models were a decision tree and two tree-based ensemble methods (extremely…
A new approach based on Machine Learning for predicting corneal curvature (K1) and astigmatism in patients with keratoconus after intracorneal ring i…
2014
Keratoconus (KC) is the most common type of corneal ectasia. A corneal transplantation was the treatment of choice until the last decade. However, intra-corneal ring implantation has become more and more common, and it is commonly used to treat KC thus avoiding a corneal transplantation. This work proposes a new approach based on Machine Learning to predict the vision gain of KC patients after ring implantation. That vision gain is assessed by means of the corneal curvature and the astigmatism. Different models were proposed; the best results were achieved by an artificial neural network based on the Multilayer Perceptron. The error provided by the best model was 0.97D of corneal curvature …
Estimating the Patient-Specific Relative Stiffness Between a Hepatic Lesion and the Liver Parenchyma
2017
This paper presents a novel non-invasive methodology to obtain the patient-specific relative stiffness between a hepatic lesion and the liver parenchyma in vivo. This relative stiffness can be used as a biomarker about the type of lesion. This biomarker together with the rest of pathological information can be used to plan a biopsy, an image-guide intervention or a radiation therapy. This relative stiffness is estimated by means of the finite element simulation of the breathing process, which is embedded in an optimization routine based on genetic algorithms. This routine was aimed at finding the patient-specific relative stiffness between a hepatic lesion and the liver parenchyma for the p…
An Augmented Reality System for the Treatment of Acrophobia: The Sense of Presence Using Immersive Photography
2006
This paper describes an augmented reality (AR) system for the treatment of acrophobia. First, the technical characteristics of the original prototype are described. Second, the capacity of the immersive photography used in the AR system to provoke sense of presence in users is tested. Forty-one participants without fear of heights walked around a staircase in both a real environment and an immersive photography environment. Immediately after their experience, participants were given the SUS questionnaire to assess their subjective sense of presence. The users' scores in the immersive photography were very high. Results indicate that the acrophobic context can be useful for the treatment of…
Estimation of the elastic parameters of human liver biomechanical models by means of medical images and evolutionary computation.
2013
This paper presents a method to computationally estimate the elastic parameters of two biomechanical models proposed for the human liver. The method is aimed at avoiding the invasive measurement of its mechanical response. The chosen models are a second order Mooney–Rivlin model and an Ogden model. A novel error function, the geometric similarity function (GSF), is formulated using similarity coefficients widely applied in the field of medical imaging (Jaccard coefficient and Hausdorff coefficient). This function is used to compare two 3D images. One of them corresponds to a reference deformation carried out over a finite element (FE) mesh of a human liver from a computer tomography image, …
Patient-specific simulation of the intrastromal ring segment implantation in corneas with keratoconus
2015
Purpose The purpose of this study was the simulation of the implantation of intrastromal corneal-ring segments for patients with keratoconus. The aim of the study was the prediction of the corneal curvature recovery after this intervention. Methods Seven patients with keratoconus diagnosed and treated by implantation of intrastromal corneal-ring segments were enrolled in the study. The 3D geometry of the cornea of each patient was obtained from its specific topography and a hyperelastic model was assumed to characterize its mechanical behavior. To simulate the intervention, the intrastromal corneal-ring segments were modeled and placed at the same location at which they were placed in the s…
Modeling the Mechanical Behavior of the Breast Tissues Under Compression in Real Time
2017
This work presents a data-driven model to simulate the mechanical behavior of the breast tissues in real time. The aim of this model is to speed up some multimodal registration algorithms, as well as some image-guided interventions. Ten virtual breast phantoms were used in this work. Their deformation during a mammography was performed off-line using the finite element method. Three machine learning models were trained with the data from those simulations. Then, they were used to predict the deformation of the breast tissues. The models were a decision tree and two ensemble methods (extremely randomized trees and random forest). Four experiments were designed to assess the performance of th…
Using Augmented Reality to Treat Phobias
2005
Virtual reality (VR) is useful for treating several psychological problems, including phobias such as fear of flying, agoraphobia, claustrophobia, and phobia to insects and small animals. We believe that augmented reality (AR) could also be used to treat some psychological disorders. AR and VR share some advantages over traditional treatments. However, AR gives a greater feeling of presence (the sensation of being there) and reality judgment (judging an experience as real) than VR because the environment and the elements the patient uses to interact with the application are real. Moreover, in AR users see their own hands, feet, and so on, whereas VR only simulates this experience. With thes…
A framework for modelling the biomechanical behaviour of the human liver during breathing in real time using machine learning
2017
Progress in biomechanical modelling of human soft tissue is the basis for the development of new clinical applications capable of improving the diagnosis and treatment of some diseases (e.g. cancer), as well as the surgical planning and guidance of some interventions. The finite element method (FEM) is one of the most popular techniques used to predict the deformation of the human soft tissue due to its high accuracy. However, FEM has an associated high computational cost, which makes it difficult its integration in real-time computer-aided surgery systems. An alternative for simulating the mechanical behaviour of human organs in real time comes from the use of machine learning (ML) techniq…
Machine Learning for Modeling the Biomechanical Behavior of Human Soft Tissue
2016
An accurate modeling of the biomechanical properties of human soft tissue is crucial in many clinical applications, such as, radiotherapy administration or surgery. The finite element method (FEM) is the usual choice to carry out such modeling due to its high accuracy. However, FEM is computationally very costly, and hence, its application in real-time or even off-line with short delays are still challenges to overcome. This paper proposes a framework based on Machine Learning to learn FEM modeling, thus having a tool able to yield results that may be sufficiently fast for clinical applications. In particular, the use of ensembles of Decision Trees has shown its suitability in modeling the …
Artificial neural networks for predicting dorsal pressures on the foot surface while walking
2012
In this work, artificial neural networks (ANNs) are proposed to predict the dorsal pressure over the foot surface exerted by the shoe upper while walking. A model that is based on the multilayer perceptron (MLP) is used since it can provide a single equation to model the exerted pressure for all the materials used as shoe uppers. Five different models are produced, one model for each one of the four subjects under study and an overall model for the four subjects. The inputs to the neural model include the characteristics of the material and the positions during a whole step of 14 pressure sensors placed on the foot surface. The goal is to find models with good generalization capabilities, (…