Traditional approaches to gathering and visualizing pain data rely on two-dimensional (2-D) human body models, where different types of sensation are recorded with various monochrome symbols. We proposean alternative that uses a three-dimensional (3-D) representation of the human body, which can be marked in color to visualize and record pain data

Chana, G

Frank, AO

Ghinea, G

Kent, S

English

Brunel University Research Archive

IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 11, NO. 5, SEPTEMBER 2007 595[4] Z. R. Yang and R. Hamer, “Bio-basis function neural networks in proteindata mining,” Curr. Pharm. Res., vol. 13, pp. 1403–1413, 2007.[5] C. Bishop, Neural Networks for Pattern Recognition. New York: OxfordUniv. Press, 1995.[6] R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis.New York: Wiley, 2002.[7] M. Li and I. K. Sethi, “Confidence-based classifier design,” PatternRecognit., vol. 39, pp. 1230–1240, 2006.[8] A. Madevska-Bogdanova, D. Nikolik, and L. Curfs, “Probabilistic SVMoutputs for pattern recognition using analytical geometry,” Neurocomput-ing, vol. 62, pp. 293–303, Dec. 2004.[9] J. Platt, “Probabilistic outputs for SVMs and comparisons toregularized likelihood methods,” in Advances in Large MarginClassifiers, P. J. Bartlett, B. Scho¨lkopf, D. Schuurmans, and A. J. Smola,Eds. Cambridge, MA: MIT Press, 1999.[10] B. Zadrozny and C. Elkan, “Transforming classifier scores into accu-rate multiclass probability estimates,” in Proc 8th Int. Conf. Knowl. Dis-cov. Data Mining (KDD’02), Edmonton, AB, Canada, Jul. 23–26, 2002,pp. 694–699.[11] R. Francki, C. Fauquet, D. Knudson, and F. Brown, “Classification andnomenclature of viruses,” Arch. Virol., vol. 2, p. 223, 1991.[12] C. Steinku¨hler, A. Urbani, L. Tomei, G. Bisdiol, M. Sardana, E. Bianchi,A. Pessi, and R. De Francesco, “Activity of purified hepatitis C virusprotease NS3 on peptide substrates,” J. Virol., vol. 70, pp. 6694–6700,1996.[13] A. Kwong, J. Kim, G. Rao, D. Lipovsek, and S. Raybuck, “Hepatitis Cvirus NS3/4A protease,” Antiviral Res., vol. 40, pp. 1–18, 1998.[14] T. Chambers, R. Weir, A. Grakoui, D. McCourt, F. Bazan, R. Fletterick,and C. Rice, “Evidence that the N-Terminal domain of nonstructural pro-tein NS3 from Yellow Fever Virus is a serine proteiase responsible forsite-specific cleavages in the viral polyprotein,” Proc. Nat. Acad. Sci.USA, vol. 87, pp. 8898–8902, 1990.[15] I. Schechter and A. Berger, “On the size of active sites in proteases.I. Papain.,” Biochem. Bio. Res. Commun., vol. 27, pp. 157–162, 1967.[16] J. Bukh, R. H. Purcell, and R. H. Miller, “At least 12 genotypes of hepatitisC virus predicted by sequence analysis of the putative E1 gene of isolatescollected worldwide,” Proc. Nat. Acad. Sci. USA, vol. 91, pp. 8234–8238,1993.[17] E. Samokhvalov, M. Hijikata, R. Gylka, D. K. Lvov, and S. Mishiro,“Full-genome nucleotide sequence of a hepatitis C virus variant (iso-late name VAT96) representing a new subtype within the geno-type 2 (arbitrarily 2k),” Virus Genes, vol. 20, pp. 183–187,2000.[18] L. Stuyver, W. Van Arnhem, A. Wyseur, F. Hernandez, E. Delaporte, andG. Maertens, “Classification of hepatitis C viruses based on phylogeneticanalysis of the envelope 1 and nonstructural 5B regions and identificationof five additional subtypes,” Proc. Nat. Acad. Sci. USA, vol. 91, pp. 10134–10138, 1994.[19] A. Narayanan, X. Wu, and Z. R. Yang, “Mining viral protease datato extract cleavage knowledge,” Bioinformat., vol. 18, pp. 5–13,2002.[20] C. Nello and J. Shaw-Taylor, An Introduction to Support Vector Machines,Cambridge, U.K.: Cambridge Univ. Press, 2000.[21] R. Thomson, T. Hodgman, Z. R. Yang, and A. Doyle, “Characterising pro-teolytic cleavage site activity using bio-basis function neural networks,”Bioinformatics, vol. 19, pp. 1741–1747, 2003.[22] Z. R. Yang and R. Thomson, “Bio-basis function neural network forprediction of protease cleavage sites in proteins,” IEEE Trans. NeuralNetw., vol. 16, no. 1, pp. 263–274, Jan. 2005.[23] M. O. Dayhoff, R. M. Schwartz, and B. C. Orcutt, “A model of evolution-ary change in proteins. matrices for detecting distant relationships,” AtlasProtein Seq. Str., vol. 5, pp. 345–358, 1978.[24] D. J. MaCkay, “A practical Bayesian framework for backpropagationnetworks,” Neural Comput., vol. 4, pp. 448–472, 1992.[25] T. Joachims, “Making large-scale SVM learning practical,” in Advances inKernel Methods—Support Vector Learning, B. Scholkopf and C. Burges,Eds. Cambridge, MA: MIT Press, 1999.[26] B. W. Matthews, “Comparison of the predicted and observed secondarystructure of T4 phage lysozyme,” Biochim. Biophys. Acta, vol. 405,pp. 442–451, 1975.[27] C. E. Metz, “Basic principles of ROC analysis,” Semin. Nucl. Med., vol. 8,pp. 283–298, 1978.Visualization of Back Pain Data—A 3-D SolutionGheorghita Ghinea, Simon Kent, Andrew O. Frank,and Gurudain Singh ChanaAbstract—Traditional approaches to gathering and visualizing pain datarely on two-dimensional (2-D) human body models, where different typesof sensation are recorded with various monochrome symbols. We proposean alternative that uses a three-dimensional (3-D) representation of thehuman body, which can be marked in color to visualize and record paindata.Index Terms—Pain drawings, three-dimensional (3-D), visualization.I. INTRODUCTIONBack pain is ubiquitous and seemingly capable of plaguing almostanyone in all cultures and ethnic groups. It is a very common reason forphysician visits; second only to the common cold. Back pain is also thesecond most recorded cause for absenteeism, with close to 120 millionworking days being lost each year due to back pain [1].In most cases, the only visual aid to assist medical staff with backpain assessment are 2-D “pain drawings” (Fig. 1), on which the patientis asked to mark the distribution and type (this being either symbol [2],[3] or color-coded [4]) of the pain being suffered. Studies have indicatedthat patient drawings have high completion rates, and in conjunctionwith a psychological evaluation, allow the physician to assess whetherthe pain is anatomic or non-anatomic [1]–[3].The focus of the work described in this paper has been theaugmentation of the traditional 2-D pain drawing with a novel,computer-based, 3-D version—in the anticipation that electronic3-D pain drawings will increase the ease with which patients recordtheir own pain, as well as provide a tool for pain data collection andmonitoring of back pain clinicians.II. 3-D PAIN DRAWINGSAfter consultations with clinicians and patients alike, it was felt that apatient would be able to better map the pain that they were experiencingonto a 3-D model of themselves rather than a 2-D one. Using a front andrear 2-D projection of a human torso makes it difficult to map pain thatis occurring at the edges of the representation. A 3-D model providesa continuous body surface which allows a practitioner, or indeed apatient, to accurately record pain. Moreover, by being able to interactwith the environment, anomalies caused by 2-D depth perception areremoved and thus, consistency is increased.In terms of back pain, an experimental study using 3-D for recon-structing spinal cord injuries reinforced the power of 3-D in medicalpractice. 3-D virtual images were constructed from performing com-puterized medical scans [5]. In this case, 3-D images were extremelybeneficial because the models could be observed from many differentviewpoints. Rotation and zooming features were combined to allowobserver navigation within the tissue. 3-D navigation provided depthperception, so spatial relationships of features could be better revealed.The same feature benefit was anticipated from devising a 3-D adapta-tion of pain drawings.Manuscript received July 27, 2006.G. Ghinea, S. Kent, and G. S. Chana are with the School of InformationSystems, Computing and Mathematics, Brunel University, Uxbridge UB8 3PH,U.K. (e-mail: george.ghinea@brunel.ac.uk; Simon.Kent@brunel.ac.uk;gurudain.chana@brunel.ac.uk).A. O. Frank is with Northwick Park Hospital, Harrow HA1 3UJ, U.K.(e-mail: andrew.frank1@btinternet.com).Digital Object Identifier 10.1109/TITB.2006.8897101089-7771/$25.00 © 2007 IEEEAuthorized licensed use limited to: Brunel University. Downloaded on June 21, 2009 at 05:19 from IEEE Xplore.  Restrictions apply.596 IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 11, NO. 5, SEPTEMBER 2007Fig. 1. Completed 2-D pain drawing.III. PROTOTYPE DESIGNThe design of the prototype was conducted in collaboration witha team of clinicians from the rheumatology department of NorthwickPark Hospital, London—potential users of the system. In brief, theidentified requirements were to: provide a 3-D model of the human body; provide fully navigational controls enabling the ability to zoom,rotate, drag for depth perception; allow individually selectable regions of the body; use color to represent different types of pain; allow the details to be saved for later analysis by physician andfor record keeping on a patient’s file; avoid the use of specialized hardware or software.It was thus felt that it would be useful if the prototype applicationcould be delivered using a web-based approach. Macromedia Directorwas used to produce the 3-D Pain Visualization application that wasdistributable via the web.To date, the interface has been used on a Windows XP PC, anApple machine running OS X and a Linux PC. In our prototypicalimplementation, the application server comprised an Apache serverconfigured to execute PHP (hypertext preprocessor) and a pain databaseimplemented using MySQL, based on previous work of ours [6].The 3-D model was developed using 3-D Studio Max 7. Basedon earlier work [2], [3], we color-coded five different types of pain(burning, aching, stabbing, pins and needles, and numbness). Initially,a model was developed of an upright human body, but later on, a sittingversion has also been created. Although a standing body might seemmore natural, work has previously been undertaken to use a PDA toallow wheelchair users to record the pain that they experience duringeveryday wheelchair use [6].This sitting model was more appropriate for this type of user. Forthe purpose of this paper, the use of different models demonstrates theflexibility of the system to allow alternative 3-D models to be usedaccording to the needs of the patient. This is to make the patient’s taskas simple as possible by providing a realistic copy of their torso. Anexample of a 3-D model with the developed interface is shown in Fig. 2.IV. EVALUATIONA. Clinical EvaluationTwo clinicians, who used existing 2-D pain drawings, were asked toreview the prototype: a back-pain specialist and a physiotherapist. BothFig. 2. Seated avatar for collection of pain data from a wheelchair user.approved of the approach and agreed that the prototype would be use-able in a clinical environment. Their observations can be summarizedas follows.1) Positive observationsa) 3-D interface covered almost all aspects of existing paindrawings.b) The levels of detail and navigation control wereimpressive.2) Negative observationsa) 3-D models needed enhancing to represent some pain lo-cations. For example, some patients experienced pain onthe outside of the hand, and there was no correspondingregion on the models developed.b) Only one type of pain could be recorded per region.c) Colors used could be an issue for visually impaired orcolor blind patients.B. Patient EvaluationThirty patients were asked to use the 3-D prototype for 10 min and tocomplete a questionnaire in order to evaluate the usability of the system.In general, the results were positive. Users were very satisfied with thenavigation (68% of users) and layout (87%) of the interface. The contextsensitive help (tooltips) that were provided, as users hovered over areasof the interface, was received with some ambivalence. Most participants(68%) were satisfied with the color scheme, although this was the onlyquestion for which negative feedback was received, correlating withthe view of the clinicians. A strong preference was expressed by allusers for “rollover feature” in which pain areas were highlighted ingrey as the mouse pointer was moved over the avatar to help the userto correctly record the pain in the correct place. Lastly, users wereasked as to what would they like to improve regarding the system. Themajority (57%) stated that the regions should have been smaller, so asto pinpoint the pain they were experiencing more accurately.V. CONCLUSIONThis paper has described a 3-D pain drawing solution to addressthe limitation of existing 2-D approaches. Evaluation has shown thatboth general users and medically qualified practitioners perceive theAuthorized licensed use limited to: Brunel University. Downloaded on June 21, 2009 at 05:19 from IEEE Xplore.  Restrictions apply.IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 11, NO. 5, SEPTEMBER 2007 5973-D approach as positive, and it was found easy to use by both groups.However, the need for further development has also been identified.Improvement is clearly needed in respect of allowing multiple typesof pain to be recorded for a particular region of the body and usingconfigurable color maps. Future work will also entail the porting ofthe prototype to wireless platforms, as well as the augmentation withanimated 3-D versions potentially capable of displaying pain variationacross time. All these efforts form an integral part of our thrust to buildenhanced clinical wireless solutions.REFERENCES[1] A. O. Frank and L. H. De Souza, “Conservative management of low backpain,” Int. J. Clin. Practice, vol. 55, no. 1, pp. 21–23, 2001.[2] H. Parker, P. L. Wood, and C. J. Main, “The use of the pain drawing as ascreening measure to predict psychological distress in chronic low backpain,” Spine, vol. 20, no. 2, pp. 236–243, 1995.[3] K. M. Prkachin, I. Schultz, J. Berkowitz, E. Hughes, and D. Hunt, “As-sessing pain behaviour of low-back pain patients in real time: Concurrentvalidity and examiner sensitivity,” Behav. Res. Therap., vol. 40, no. 5,pp. 595–607, 2002.[4] R. Masferrer, V. Prendergast, and P. Hagell, “Colored pain drawings:Preliminary observations in neurosurgical practice,” Eur. J. Pain, vol. 7,no. 3, pp. 213–217, 2002.[5] B. S. Duerstock, C. L. Bajaj, V. Pascucci, D. Schikore, K. N. Lin, andR. B. Borgens, “Advances in three-dimensional reconstruction of the ex-perimental spinal cord injury,” Comput. Med. Imag. Graph., vol. 24, no. 6,pp. 389–406, 2000.[6] T. Serif and G. Ghinea, “Recording of time-varying back-pain data: Awireless solution,” IEEE Trans. Inf. Technol. Biomed., vol. 9, no. 3,pp. 447–458, Sep. 2005.Collaborative Work During Interventional RadiologicalProcedures Based on a Multicast Satellite-TerrestrialNetworkLefteris G. Gortzis, Homer Papadopoulos, Theo A. Roelofs,Stefan Rakowsky, Dimitris Karnabatidis, Dimitris Siablis,Constantinos Makropoulos, George Nikiforidis, and Georgi GraschewAbstract—Collaboration is a key requirement in several contemporaryinterventional radiology procedures (IRPs). This work proposes a multicasthybrid satellite system capable of supporting advanced IRP collaboration,and evaluates its feasibility and applicability. Following a detailed IRP re-quirements study, we have developed a system which supports IRP collab-oration through the employment of a hybrid satellite-terrestrial network,a prototype multicast version of wavelet based interactive communicationsystem (WinVicos) application, and a partition aggregation and conditionalcoding (PACC) wavelet codec. A semistructured questionnaire was alsoused to receive evaluative feedback from collaborating participants. Thedepartments of interventional radiology of University Hospital of Patras,Greece and of Charite Hospital of Berlin, Germany have been connected onthe system. Eight interventional radiologists and a vascular surgeon partic-ipated periodically in three satellite-terrestrial “fully collaborative” IRPs(average time 90 min) of high complexity and in four terrestrial educationalsessions with great success, evidenced by considerable improving the IRPoutcomes (clinical and educational). In case of high complexity, where thesimultaneous presence of remote interventional expert and/or surgeon isrequired, advanced collaboration among staff of geographically dispersedinternational centers is feasible via integration of existing networking andother technologies.Index Terms—e-health, multicast collaboration, radiology, satellite,wavelet encoding.I. INTRODUCTIONModern interventional radiological procedures (IRPs) require ad hocmultidisciplinary collaboration to improve the clinical and educationaloutcomes [1].The real-time IRP collaboration demands effective visual recon-struction of remote Catheterization Lab (CL), data utilization features,satisfactory bandwidth, a clinically accepted video compression, andan integrated scenario. However, the few existing collaborative sys-tems are not capable of supporting the main requirements, as manychallenges limit their effectiveness [2], [3], i.e., hybrid networking [4],poor bandwidth [5], end-to-end delay [6], [7], and imaging quality [8].This work aims to support collaboration during real-time IRPsamong experts located at international referral centers worldwide byutilizing a satellite-terrestrial system. This system addresses the previ-ous challenges by multicasting the required wavelet-based video feedsand the use of interactive features within a common multiscreen in-terface. The system’s feasibility and applicability has been evaluatedin three satellite-terrestrial “fully collaborative” IRPs with an averageduration time of 90 min and in four terrestrial collaborative educationalsessions.II. MATERIALS AND METHODSA. System OverviewThe present system comprises four collaborative nodes, which arelocated at three terrestrial sites: two nodes at CL of the UniversityHospital of Patras, Patras, Greece, one node at Charite Hospital, Berlin,Germany, and one intermediate node at National Centre for ScientificResearch (NCSR) Demokritos, Athens, Greece, as shown in Fig. 1.To support IRP collaboration, the system employs a common mul-tiscreen interface, and multicasts simultaneously the appropriate videofeeds. These video feeds are presented in pop-up windows, as shown inFig. 2. Particularly, the video feeds are able to display 1) the angio-unitoutput signal, which is captured to support the visual reconstruction ofthe intervention area; 2) the CL ceiling camera output signal, whichis captured to support the CL environment visual reconstruction, e.g.,hand movements of the interventionist performing the procedure etc.;3) the document camera output signal, which is captured to visualizethe MRI, or MRCP; and 4) the collaborators’ cameras output signals,which are captured separately to support conference capabilities.It should be noted that the video transmission parameters can be con-figured separately according to each node’s capabilities (ten differentframe parameters ranging from 128× 96 to 640× 480 are supported).Manuscript received September 1, 2006; revised December 11, 2006.L. G. Gortzis and G. Nikiforidis are with the Telemedicine Unit, Departmentof Medical Physics, University of Patras Medical School, 26500 Patras, Greece(e-mail: gortzis@med.upatras.gr).H. Papadopoulos and C. Makropoulos are with the Division of Applied Tech-nologies, National Centre for Scientific Research “Demokritos,” 15310 Athens,Greece.T. A. Roelofs, S. Rakowsky, and G. Graschew are with the Surgical ResearchUnit OP 2000, Robert-Roessle-Klinik and Max-Delbrueck Center for MolecularMedicine, University Hospital Charite´, Humboldt University of Berlin, 10117Berlin, Germany.D. Karnabatidis and D. Siablis are with the Department of InterventionalRadiology, University Hospital of Patras, Patras 26500, Greece.Digital Object Identifier 10.1109/TITB.2007.8947241089-7771/$25.00 © 2007 IEEEAuthorized licensed use limited to: Brunel University. Downloaded on June 21, 2009 at 05:19 from IEEE Xplore.  Restrictions apply.

Assessing pain behaviour of low-back pain patients in real time: Concurrent validity and examiner sensitivity,”

Colored pain drawings: Preliminary observations in neurosurgical practice,”

Conservative management of low back pain,”

Recording of time-varying back-pain data: A wireless solution,”

The use of the pain drawing as a screening measure to predict psychological distress in chronic low back pain,”

Visualization of back pain data-A 3-D solution

Visualization of back pain data-A 3-D solution

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