Computer Methods and Programs in Biomedicine RSS feed: Current Issue. To encourage the development of formal computing methods, and their application in biomedical research and medical practice, by illustration of fundamental principles in biomedical informatics research; to stimulate basic research into application software design; to report the state of research of biomedical information processing projects; to report new computer methodologies applied in biomedical areas; the eventual distribution of demonstrable software to avoid duplication of effort; to provide a forum for discussion and improvement of existing software; to optimize contact between national organizations and regional user groups by promoting an international exchange of information on formal methods, standards and software in biomedicine. Computer Methods and Programs in Biomedicine covers computing methodology and software systems derived from computing science for implementation in all aspects of biomedical research and medical practice. It is designed to serve: biochemists; biologists; geneticists; immunologists; neuroscientists; pharmacologists; toxicologists; clinicians; edipemiologists; psychiatrists; psychologists; cardiologists; chemists; (radio)physicists; computer scientists; programmers and systems analysts; biomedical, clinical, electrical and other engineers; teachers of medical informatics and users of educational software. http://www.cmpbjournal.com/?rss=yesElsevier Inc.en © 2012 Published by Elsevier Inc. All rights reserved. Computer Methods and Programs in Biomedicine0169-26071053March 2012 © 2012 Published by Elsevier Inc. All rights reserved. healthpermissions@elsevier.comhttp://www.cmpbjournal.com/article/PIIS0169260712000193/abstract?rss=yesEditorial Board10.1016/S0169-2607(12)00019-3Computer Methods and Programs in Biomedicine 105, 3 (2012)2012-03-01Computer Methods and Programs in Biomedicine2012-03-011053S0169-2607(12)X0003-8CO2CO2http://www.cmpbjournal.com/article/PIIS0169260711002562/abstract?rss=yesAbstract: The purpose of this study is to develop a clinical decision support system based on machine learning (ML) algorithms to help the diagnostic of chronic obstructive pulmonary disease (COPD) using forced oscillation (FO) measurements. To this end, the performances of classification algorithms based on Linear Bayes Normal Classifier, K nearest neighbor (KNN), decision trees, artificial neural networks (ANN) and support vector machines (SVM) were compared in order to the search for the best classifier. Four feature selection methods were also used in order to identify a reduced set of the most relevant parameters. The available dataset consists of 7 possible input features (FO parameters) of 150 measurements made in 50 volunteers (COPD, n=25; healthy, n=25). The performance of the classifiers and reduced data sets were evaluated by the determination of sensitivity (Se), specificity (Sp) and area under the ROC curve (AUC). Among the studied classifiers, KNN, SVM and ANN classifiers were the most adequate, reaching values that allow a very accurate clinical diagnosis (Se>87%, Sp>94%, and AUC>0.95). The use of the analysis of correlation as a ranking index of the FOT parameters, allowed us to simplify the analysis of the FOT parameters, while still maintaining a high degree of accuracy. In conclusion, the results of this study indicate that the proposed classifiers may contribute to easy the diagnostic of COPD by using forced oscillation measurements.Machine learning algorithms and forced oscillation measurements applied to the automatic identification of chronic obstructive pulmonary diseaseJorge L.M. Amaral, Agnaldo J. Lopes, José M. Jansen, Alvaro C.D. Faria, Pedro L. Melo10.1016/j.cmpb.2011.09.009Computer Methods and Programs in Biomedicine 105, 3 (2012)2012-03-01Computer Methods and Programs in Biomedicine2012-03-011053S0169-2607(12)X0003-8Section I: Methodology183193http://www.cmpbjournal.com/article/PIIS0169260711002690/abstract?rss=yesAbstract: Predicting significant fibrosis or cirrhosis in patients with hepatitis C virus has persistently preoccupied the research agenda of many specialized research centers. Many studies have been conducted to evaluate the use of readily available laboratory tests to predict significant fibrosis or cirrhosis with the purpose to substantially reduce the number of biopsies performed. Although many of them reported significant predictive values of several serum markers for the diagnosis of cirrhosis, none of these diagnostic techniques was successful in accurately predicting early stages of liver fibrosis. Therefore, in this study a single stage classification model and a multistage stepwise classification model based on Neural Network, Decision Tree, Logistic Regression, and Nearest Neighborhood clustering, have been developed to predict individual's liver fibrosis degree. Results showed that the area under the receiver operator curve (AUROC) values of the multistage model ranged from 0.874 to 0.974 which is a higher range than what is reported in current researches with similar conditions.Single stage and multistage classification models for the prediction of liver fibrosis degree in patients with chronic hepatitis C infectionAhmed M. Hashem, M. Emad M. Rasmy, Khaled M. Wahba, Olfat G. Shaker10.1016/j.cmpb.2011.10.005Computer Methods and Programs in Biomedicine 105, 3 (2012)2012-03-01Computer Methods and Programs in Biomedicine2012-03-011053S0169-2607(12)X0003-8Section I: Methodology194209http://www.cmpbjournal.com/article/PIIS0169260711002586/abstract?rss=yesAbstract: In the clinical practice of psychiatry, presence or absence of particular symptoms is based on the subjective interpretation, by the clinician, of mental and behavioural descriptions offered by the patient. However, this subjectivity that characterizes the diagnostic decision making process may limit the reliability of diagnosis. In this current study, the pairwise comparisons (PC) method is used to investigate whether the psychometric properties of a medical screening questionnaire can be improved. The pilot data described herein did indeed demonstrate that modest improvements in diagnostic accuracy could be achieved using PC, and provides early evidence that the inconsistency produced by subjective clinical ratings can be reduced using this method, thus providing impetus for further investigation.Improving the medical scale predictability by the pairwise comparisons method: Evidence from a clinical data studyTamar Kakiashvili, Waldemar W. Koczkodaj, Marc Woodbury-Smith10.1016/j.cmpb.2011.09.011Computer Methods and Programs in Biomedicine 105, 3 (2012)2012-03-01Computer Methods and Programs in Biomedicine2012-03-011053S0169-2607(12)X0003-8Section I: Methodology210216http://www.cmpbjournal.com/article/PIIS0169260711002574/abstract?rss=yesAbstract: The temporomandibular joint (TMJ) is one of the most important and complex joints of the body and its pathologies affect a great percentage of the human population. The simulation of the TMJ behavior during opening, closing and chewing movements can be very useful to the understanding of this articulation by physicians, helping them to prevent or fix problems due to accidents or diseases. This work proposes a model to simulate the human TMJ behavior based on the concept of two interdependent joints. The model was conceived using multimodal information acquired from CT and MRI images of a live person, as well as motion data acquired from this same person with a magnetic motion capture device. Simulation of movement of other TMJs, based on different morphology of bones and teeth, is obtained by adapting the regular captured motion data through collision detection and treatment methods. The proposed model was evaluated through image registration techniques by comparing our simulated results with real, captured motion data. We also validate the model showing how it can be used to predict TMJ behavior in the presence of different – normal or abnormal – bones and teeth morphologies.Simulation of the human TMJ behavior based on interdependent joints topologyMarta B. Villamil, Luciana P. Nedel, Carla M.D.S. Freitas, Benoit Macq10.1016/j.cmpb.2011.09.010Computer Methods and Programs in Biomedicine 105, 3 (2012)2012-03-01Computer Methods and Programs in Biomedicine2012-03-011053S0169-2607(12)X0003-8Section I: Methodology217232http://www.cmpbjournal.com/article/PIIS0169260711002434/abstract?rss=yesAbstract: Uncomplicated urinary tract infection (uUTI) is a bacterial infection that affects individuals with normal urinary tracts from both structural and functional perspective. The appropriate antibiotics and treatment suggestions to individuals suffer of uUTI is an important and complex task that demands a special attention. How to decrease the unsafely use of antibiotics and their consumption is an important issue in medical treatment. Aiming to model medical decision making for uUTI treatment, an innovative and flexible approach called fuzzy cognitive maps (FCMs) is proposed to handle with uncertainty and missing information. The FCM is a promising technique for modeling knowledge and/or medical guidelines/treatment suggestions and reasoning with it. A software tool, namely FCM-uUTI DSS, is investigated in this work to produce a decision support module for uUTI treatment management. The software tool was tested (evaluated) in a number of 38 patient cases, showing its functionality and demonstrating that the use of the FCMs as dynamic models is reliable and good. The results have shown that the suggested FCM-uUTI tool gives a front-end decision on antibiotics’ suggestion for uUTI treatment and are considered as helpful references for physicians and patients. Due to its easy graphical representation and simulation process the proposed FCM formalization could be used to make the medical knowledge widely available through computer consultation systems.Fuzzy cognitive map software tool for treatment management of uncomplicated urinary tract infectionElpiniki I. Papageorgiou10.1016/j.cmpb.2011.09.006Computer Methods and Programs in Biomedicine 105, 3 (2012)2012-03-01Computer Methods and Programs in Biomedicine2012-03-011053S0169-2607(12)X0003-8Section II: Systems and Programs233245http://www.cmpbjournal.com/article/PIIS0169260711002446/abstract?rss=yesAbstract: Cone Beam Computed Tomography (CBCT) is widely used in radiation therapy for verifying treatment areas, since it provides three-dimensional image reconstruction of those tumour regions under inspection. However, organ motion is problematic during the scanning process, it causes motion artefacts on the CBCT image and can lead to mispositioning for the subsequent treatment. Moreover, patient dose is also considerable and there is a need for methods which yield acceptable image quality with as few X-ray images as possible. Although methods have been developed to handle limited projection data, such as the Algebraic Reconstruction Technique (ART); Simultaneous ART (SART); and Ordered-Subset SART (OS-SART), this study applied motion compensation to these reconstruction techniques. Root Mean Square Error (RMSE) of image is calculated to study the convergence of reconstructed images compared with the truth image. When motion was applied to a phantom and the motion compensation was used to account for the motion, the results showed that motion compensation improved the quality of CBCT image, when compared to uncompensated images. Furthermore, the experiments suggested that minimising phase error, for breathing models, was more important than minimising amplitude error.Cone Beam CT using motion-compensated algebraic reconstruction methods with limited dataT. Pengpan, W. Qiu, N.D. Smith, M. Soleimani10.1016/j.cmpb.2011.09.007Computer Methods and Programs in Biomedicine 105, 3 (2012)2012-03-01Computer Methods and Programs in Biomedicine2012-03-011053S0169-2607(12)X0003-8Section II: Systems and Programs246256http://www.cmpbjournal.com/article/PIIS0169260711002665/abstract?rss=yesAbstract: This paper describes feature extraction methods using higher order statistics (HOS) of wavelet packet decomposition (WPD) coefficients for the purpose of automatic heartbeat recognition. The method consists of three stages. First, the wavelet package coefficients (WPC) are calculated for each different type of ECG beat. Then, higher order statistics of WPC are derived. Finally, the obtained feature set is used as input to a classifier, which is based on k-NN algorithm. The MIT-BIH arrhythmia database is used to obtain the ECG records used in this study. All heartbeats in the arrhythmia database are grouped into five main heartbeat classes. The classification accuracy of the proposed system is measured by average sensitivity of 90%, average selectivity of 92% and average specificity of 98%. The results show that HOS of WPC as features are highly discriminative for the classification of different arrhythmic ECG beats.Feature extraction for ECG heartbeats using higher order statistics of WPD coefficientsYakup Kutlu, Damla Kuntalp10.1016/j.cmpb.2011.10.002Computer Methods and Programs in Biomedicine 105, 3 (2012)2012-03-01Computer Methods and Programs in Biomedicine2012-03-011053S0169-2607(12)X0003-8Section III: Experiences with Methods, Systems and Programs257267