guest editorial introduction to the special section on computational intelligence in medical systems

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    Guest EditorialIntroduction to the Special Section on Computational

    Intelligence in Medical Systems


    R ECENT technological advances in medicine have facil-itated the development of complex biomedical systemsincluding sophisticated biomedical signal devices and instru-ments, medical imaging equipment, and computer-aided diag-nosis (CAD) tools enabling the better delivery of healthcareservices. In parallel, computational intelligence, incorporat-ing neural computing, fuzzy systems, evolutionary computing,and more recently, rough sets, and autoimmune systems haveemerged as promising tools for the development, application,and implementation of intelligent systems.

    In the last ten years, there has been a significant effort inthe application of computational intelligent techniques in nu-merous biomedical systems. These cover applications in med-ical decision-making [1], biosignal analysis, and biomedicalengineering at large [2], medical imaging [3][5], bioinformat-ics [6], [7], and others. All these systems underlie the impact ofthese technologies in the biomedical domain.

    The aim of this special issue is to focus on the most recentapplications of computational intelligent systems in medicine.Papers in this special issue cover innovative applications of com-putational intelligence in the following physiological systems:skin, skeletal, muscular, central nervous, peripheral nervous,systems of special senses (eye), cardiovascular, respiratory, andreproductive.

    A total of 45 papers were submitted for this special issuethat were reviewed by at least three reviewers. Following therecommendations of the guest editors and the Editor-in-Chief,19 papers were accepted for publication. The accepted paperswere organized under the topics: General, Computational Biol-ogy, Biosignal Analysis, and Medical Imaging, with two, three,seven, and seven papers in each topic, respectively. Some ofthese papers (10 in total) have been published earlier by mistake,unfortunately in previous issues of the IEEE TRANSACTIONS ONINFORMATION TECHNOLOGY IN BIOMEDICINE. All the acceptedpapers are briefly summarized in the following section.

    It is generally accepted that, nowadays, health services arefacing a number of complex interacting and multifactorialchallenges [8]. To address these issues from the informationand communication technologies (ICTs) perspective, the WorldHealth Assembly (WHA) adopted an eHealth Strategy for theWorld Health Organization (WHO) [9]. The resolution docu-mented that the use of ICT for health is one of the most rapidlygrowing application areas in health today. Moreover, it was pro-posed that automated or semiautomated systems that support

    Digital Object Identifier 10.1109/TITB.2009.2030025

    decision-making in a clinical environment would be very usefulfor the better support of healthcare services.

    In parallel with the WHO activities, the European Commis-sion (EC) in 2004 adopted the eHealth action plan [10], as wellas subsequent directives [11], that cover a wide spectrum ofeHealth services, ranging from cross-border interoperability ofelectronic health record systems, to electronic prescriptions andhealth cards, to new information systems that are targeting toreduce waiting times and errors to facilitate a more harmoniousand complementary European approach to eHealth. Theseinitiatives, as stated in the Prague Declaration of the EUMember States, stress the need to keep the momentum sothat the potential advantages of gradual deployment of ICTin the health sector are not compromised by barriers of legal,technical, economic, or any other nature. At the same time, itis considered crucial that the benefits of eHealth applicationsand services are further enhanced and properly distributedamong all the relevant stakeholders, patients and healthcareprofessionals, society, and the economy.

    To facilitate the provision of better, and more efficient andeffective eHealth services as documented before, sophisticatedand advanced medical systems based on computational intel-ligence have to be developed. Although significant steps havebeen carried out in this direction in the last two decades, theneed still exists that intelligent medical systems be developed,covering a wider spectrum of services, and most importantly, bethoroughly evaluated before their deployment in clinical prac-tice. The papers in this special issue cover a wide range ofapplications, demonstrating the promising potential of compu-tational intelligence in medical systems.


    A. GeneralThe paper by King et al. [12] (appearing in this issue) deals

    with the development of a wireless sensor glove for the assess-ment of surgical skills. The paper presents a wireless sensorplatform for the capture of laparoscopic hand gesture data anda hidden Markov model (HMM) based analysis framework foroptimal sensor selection and placement. Detailed experimentalvalidation is provided to illustrate how the proposed methodcan be used to assess surgical performance improvement overrepeated training.

    The paper by Sampat et al. [13] proposes an empiricalmethodology for the assessment of three-class classifiers. Theassessment of classifier performance is critical for the fair com-parison of methods, including considering alternative models orparameters during system design. For two-class classification

    1089-7771/$26.00 2009 IEEE


    problems, receiver operating characteristic analysis provides aclear and concise assessment methodology for reporting perfor-mance and comparing competing systems. While several meth-ods have been proposed for assessing the performance of mul-ticlass problems, none has been widely accepted. Based on 4three-class case studies and the three popular classification tech-niques, the following conclusions were derived: 1) the methodproposed by Scurfield provides the most detailed description ofclassifier performance and insight about the sources of error ina given classification task, and 2) the methods proposed by Heand Nakas also have great practical utility as they provide boththe volume under a three-class receiver operating characteristicsurface (VUS) and an estimate of the variance of the VUS. Theseestimates can be used to statistically compare two classificationalgorithms.

    B. Computational BiologyIn their paper, Tang et al. [14] present a new algorithm, Fuzzy

    Granular Support Vector MachineRecursive Feature Elimi-nation (FGSVM-RFE), that was designed for the selection ofmultiple highly informative gene subsets for cancer classifica-tion and diagnosis. Empirical studies on three public datasetsdemonstrated that FGSVM-RFE outperforms state-of-the-artapproaches. Specifically, the independent testing accuracy forthe prostate cancer dataset was significantly improved, usingonly eight genes, which were annotated by Onto-Express to bebiologically meaningful.

    In their review, Oulas et al. [15] present an overview ofexisting computational methods for identifying micro-RNA(miRNA) genes and assessing their expression levels in can-cer. The discovery of thousands of genes that produce noncod-ing RNA (ncRNA) transcripts in the past few years suggestedthat the molecular biology of cancer is much more complex.MicroRNAs (miRNAs), an important group of ncRNAs, haverecently been associated with tumorigenesis by acting eitheras tumor suppressors or oncogenes. Given that the experimentalprediction of miRNA genes is a slow process, because of the dif-ficulties of cloning ncRNAs, experimental approaches providea number of computational tools trained to recognize featuresof the biogenesis of miRNAs that have significantly aided inthe prediction of new miRNA candidates. Computational ap-proaches provide valuable clues as to which are the dominantfeatures that characterize these regulatory units and which genesare their most likely targets. Moreover, through the use of high-throughput expression profiling methods, many molecular sig-natures of miRNA deregulation in human tumors have emerged.

    The paper Computer-aided diagnosis of capillary thyroidcarcinoma using an artificial immune system by Delibasiset al. [16] (appearing in this issue) proposes the utilization of aCAD system based on the artificial immune systems (AISs) toassist the task of thyroid malignancy diagnosis. The proposed al-gorithm is applied on fine-needle aspiration data from 2016 sub-jects with verified diagnosis, and has exhibited average speci-ficity higher than 99%, 90% sensitivity, and 98.5% accuracy.

    C. Biosignal AnalysisThe paper by Lai et al. [17] (appearing in this issue), Com-

    putational intelligence in gait analysis: A perspective on current

    applications and future challenges, surveys current signal pro-cessing and computational intelligence methodologies followedby gait applications ranging from normal gait studies, disorderdetection to artificial gait simulation. In this paper, recent sys-tems focusing on the existing challenges and issues involved inmaking them successful have been described. Moreover, new re-search in sensor technologies for gait is examined, which couldbe combined with these intelligent systems to develop moreeffective healthcare solutions.

    The paper by Apiletti et al. [18] presents a flexible frameworkthat performs real-time analysis of physiological data to moni-tor the subject in his/her daily activities in the hospital environ-ment. The framework supports the ubiquitous monitoring thatcould also be executed on mobile devices for different diseases.The effectiveness and flexibility of the proposed framework hasbeen demonstrated on 64 patients affected by different criticalillnesses in the intensi


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