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Evaluation of Interactive Visualization on Mobile Computing Platforms for Selection of Deep Brain Stimulation Parameters
Jan. 2013 (vol. 19 no. 1)
pp. 108-117
C. R. Butson, Depts. of Neurology & Neurosurg., Med. Coll. of Wisconsin, Milwaukee, WI, USA
G. Tamm, DFKI, Saarbrucken, Germany
S. Jain, Dept. of Neurology, Med. Coll. of Wisconsin, Milwaukee, WI, USA
T. Fogal, Interactive Visualization & Data Anal. Group, Intel Visual Comput. Inst., Saarbrucken, Germany
J. Kruger, DFKI, Saarbrucken, Germany
In recent years, there has been significant growth in the use of patient-specific models to predict the effects of neuromodulation therapies such as deep brain stimulation (DBS). However, translating these models from a research environment to the everyday clinical workflow has been a challenge, primarily due to the complexity of the models and the expertise required in specialized visualization software. In this paper, we deploy the interactive visualization system ImageVis3D Mobile, which has been designed for mobile computing devices such as the iPhone or iPad, in an evaluation environment to visualize models of Parkinson's disease patients who received DBS therapy. Selection of DBS settings is a significant clinical challenge that requires repeated revisions to achieve optimal therapeutic response, and is often performed without any visual representation of the stimulation system in the patient. We used ImageVis3D Mobile to provide models to movement disorders clinicians and asked them to use the software to determine: 1) which of the four DBS electrode contacts they would select for therapy; and 2) what stimulation settings they would choose. We compared the stimulation protocol chosen from the software versus the stimulation protocol that was chosen via clinical practice (independent of the study). Lastly, we compared the amount of time required to reach these settings using the software versus the time required through standard practice. We found that the stimulation settings chosen using ImageVis3D Mobile were similar to those used in standard of care, but were selected in drastically less time. We show how our visualization system, available directly at the point of care on a device familiar to the clinician, can be used to guide clinical decision making for selection of DBS settings. In our view, the positive impact of the system could also translate to areas other than DBS.
Index Terms:
neurophysiology,computational complexity,data visualisation,decision making,diseases,interactive systems,mobile computing,clinical decision making,mobile computing platforms,deep brain stimulation parameters,patient-specific models,neuromodulation therapies,clinical workflow,model complexity,visualization software,interactive visualization system,ImageVis3D mobile,iPhone,iPad,Parkinsons disease patients,DBS therapy,movement disorders clinicians,DBS electrode contacts,stimulation protocol,Satellite broadcasting,Mobile communication,Mobile handsets,Rendering (computer graphics),Electrodes,Computational modeling,Data visualization,clinical decision making,neurophysiology,computational complexity,data visualisation,decision making,diseases,interactive systems,mobile computing,clinical decision making,mobile computing platforms,deep brain stimulation parameters,patient-specific models,neuromodulation therapies,clinical workflow,model complexity,visualization software,interactive visualization system,ImageVis3D mobile,iPhone,iPad,Parkinsons disease patients,DBS therapy,movement disorders clinicians,DBS electrode contacts,stimulation protocol,Satellite broadcasting,Mobile communication,Mobile handsets,Rendering (computer graphics),Electrodes,Computational modeling,Data visualization,Parkinson's disease,Biomedical and medical visualization,mobile and ubiquitous visualization,computational model
Citation:
C. R. Butson, G. Tamm, S. Jain, T. Fogal, J. Kruger, "Evaluation of Interactive Visualization on Mobile Computing Platforms for Selection of Deep Brain Stimulation Parameters," IEEE Transactions on Visualization and Computer Graphics, vol. 19, no. 1, pp. 108-117, Jan. 2013, doi:10.1109/TVCG.2012.92
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