Ewa Pietka

Computation Intelligence in Medicine&#8212;Data Analysis and Modelling

Computational fluid dynamics techniques were used to investigate the hemodynamic effect
of unequal anterior cerebral artery flow rates on the anterior cerebral and anterior communicating
artery (ACA-ACOM) bifurcation. Hemodynamics have long been implicated as a major
factor in cerebrovascular disease. Using an idealized 2D symmetric model of the ACA-ACOM
geometry, the flow field and wall shear stress (WSS) at the bifurcation regions are assessed
for pulsatile inflows with left to right flow ratios of 1:1, 2:1, 3:1, and 4:1. Unequal flow rates
through the ACA parent arteries result in bifurcation of the higher flow parent stream and a
shifting of the impingement points along the A2-ACOM adjoining wall toward the contralateral ACA. Cross-flow through the ACOM is generally unstable and results in increased WSS
at the impingement region from the higher flow parent artery and a double amplitude peak
in the WSS at the contralateral bifurcation region from local recirculation effects. These results
suggest that asymmetry in ACA flow rates result in increased hemodynamic stresses at the ACA-ACOM bifurcation regions and suggest a possible factor for vessel weakening and
aneurysm formation.

Thomas Rau

Xing He

Prem Venugopal

Fernando Vi&#241;uela

Gary Duckwiler

Daniel J. Valentino

Hemodynamic Effect of Unequal Anterior Cerebral Artery Flow 
                        Rates on the Anterior Communicating Artery Bifurcation: A Computational 
                        Fluid Dynamics Study

One of the clinical examinations performed to evaluate the autonomic nervous system (ANS) activity is the tilt test, which consists in studying the cardiovascular response to the change of a patient's position from a supine to a head-up position. The analysis of heart rate variability signals during tilt tests has been shown to be useful for risk stratification and diagnosis on different pathologies. However, the interpretation of such signals is a difficult task. The application of physiological models to assist the interpretation of these data has already been proposed in the literature, but this requires, as a previous step, the identification of patient-specific model parameters. In this paper, a model-based approach is proposed to reproduce individual heart rate signals acquired during tilt tests. A new physiological model adapted to this problem and coupling the ANS, the cardiovascular system (CVS), and global ventricular mechanics is presented. Evolutionary algorithms are used for the identification of patient-specific parameters in order to reproduce heart rate signals obtained during tilt tests performed on eight healthy subjects and eight diabetic patients. The proposed approach is able to reproduce the main components of the observed heart rate signals and represents a first step toward a model-based interpretation of these signals.

Virginie Le Rolle

Alfredo I. Hern&#225;ndez

Pierre-Yves Richard 

Guy Carrault

An Autonomic Nervous System Model Applied to
                         the Analysis of Orthostatic Tests

An estimation methodology is presented to determine the breast tumor parameters using the surface temperature profile that may be obtained by infrared thermography. The estimation methodology involves evolutionary algorithms using artificial neural network (ANN) and genetic algorithm (GA). The ANN is used to map the relationship of tumor parameters (depth, size, and heat generation) to the temperature profile over the idealized breast model. The relationship obtained from ANN is compared to that obtained by finite element software. Results from ANN training/testing were in good agreement with those obtained from finite element model. After ANN validation, GA is used to estimate tumor parameters by minimizing a fitness function involving comparing the temperature profiles from simulated or clinical data to those obtained by ANN. Results show that it is possible to determine the depth, diameter, and heat generation rate from the surface temperature data (with 5&#37; random noise) with good accuracy for the 2D model. With 10&#37; noise, the accuracy of estimation deteriorates for deep-seated tumors with low heat generation. In order to further develop this methodology for use in a clinical scenario, several aspects such as 3D breast geometry and the effects of nonuniform cooling should be considered in future investigations.

Manu Mital

Ramana M. Pidaparti

Breast Tumor Simulation and Parameters Estimation Using Evolutionary Algorithms

Experimental and numerical methods are used to explore the stresses generated around bone screws used in rigid internal fixation of mandibular fractures. These results are intended to aid in decisions concerning both the design and the use of these bone screws. A finite element (FE) model of a human mandible is created with a fixated fracture in the parasymphyseal region. The mandibular model is anatomically loaded, and the forces exerted by the fixation plate onto the simplified screws are obtained and transferred to another finite element submodel of a screw implant embedded in a trilaminate block with material properties of cortical and cancellous bone. The stress in the bone surrounding the screw implant is obtained and compared for different screw configurations. The submodel analyses are further compared to and validated with simple axial experimental and numerical screw pull-out models. Results of the screw FE analysis (FEA) submodel show that a unicortical screw of 2.6 mm major diameter and 1.0 mm pitch will cause less bone damage than a bicortical screw of 2.3 mm major diameter and 1.0 mm pitch. The results of this study suggest that bicortical drilling can be avoided by using screws of a larger major diameter.

Naresh Chaudhary

Scott T. Lovald

Jon Wagner

Tariq Khraishi

Bret Baack

Experimental and Numerical Modeling of Screws Used for Rigid Internal Fixation of Mandibular Fractures

Screening mammograms is a repetitive task that causes fatigue and eye strain since for every thousand cases analyzed by a radiologist, only 3–4 are cancerous and thus an abnormality may be overlooked. Computer-aided detection (CAD) algorithms were developed to assist radiologists in detecting mammographic lesions. In this paper, a computer-aided detection and diagnosis (CADD) system for breast cancer is developed. The framework is based on combining principal component analysis (PCA), independent component analysis (ICA), and a fuzzy classifier to identify and label suspicious regions. This is a novel approach since it uses a fuzzy classifier integrated into the ICA model. Implemented and tested using MIAS database. This algorithm results in the classification of a mammogram as either normal or abnormal. Furthermore, if abnormal, it differentiates it into a benign or a malignant tissue. Results show that this system has 84.03% accuracy in detecting all kinds of abnormalities and 78% diagnosis accuracy.

Ikhlas Abdel-Qader

Fadi Abu-Amara

A Computer-Aided Diagnosis System for Breast Cancer Using Independent Component Analysis and Fuzzy Classifier

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Modelling and Simulation in Engineering

Modelling and Simulation: Computational Intelligence in Medicine

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Articles

Computation Intelligence in Medicine—Data Analysis and Modelling

Hemodynamic Effect of Unequal Anterior Cerebral Artery Flow Rates on the Anterior Communicating Artery Bifurcation: A Computational Fluid Dynamics Study

An Autonomic Nervous System Model Applied to the Analysis of Orthostatic Tests

Breast Tumor Simulation and Parameters Estimation Using Evolutionary Algorithms

Experimental and Numerical Modeling of Screws Used for Rigid Internal Fixation of Mandibular Fractures

A Computer-Aided Diagnosis System for Breast Cancer Using Independent Component Analysis and Fuzzy Classifier