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  • 1.
    Grip, Helena
    et al.
    Department of Biomedical Engineering and Informatics, University Hospital, Umeå, Sweden.
    Öhberg, Fredrik
    Department of Biomedical Engineering and Informatics, University Hospital, Umeå, Sweden.
    Wiklund, Urban
    Department of Biomedical Engineering and Informatics, University Hospital, Umeå, Sweden.
    Sterner, Ylva
    Community Medicine and Rehabilitation, University Hospital, Umeå, Sweden.
    Karlsson, J. Stefan
    Department of Biomedical Engineering and Informatics, University Hospital, Umeå, Sweden.
    Gerdle, Björn
    Linköping University, Department of Clinical and Experimental Medicine, Rehabilitation Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Medicine, Pain and Rehabilitation Centre.
    Classification of Neck Movement Patterns Related to Whiplash-Associated Disorders Using Neural Networks2003In: IEEE transactions on information technology in biomedicine, ISSN 1089-7771, E-ISSN 1558-0032, Vol. 7, no 4, p. 412-418Article in journal (Refereed)
    Abstract [en]

    This paper presents a new method for classification of neck movement patterns related to Whiplash-associated disorders (WAD) using a resilient backpropagation neural network (BPNN). WAD are a common diagnosis after neck trauma, typically caused by rear-end car accidents. Since physical injuries seldom are found with present imaging techniques, the diagnosis can be difficult to make. The active range of the neck is often visually inspected in patients with neck pain, but this is a subjective measure, and a more objective decision support system, that gives a reliable and more detailed analysis of neck movement pattern, is needed. The objective of this study was to evaluate the predictive ability of a BPNN, using neck movement variables as input. Three-dimensional (3-D) neck movement data from 59 subjects with WAD and 56 control subjects were collected with a ProReflex system. Rotation angle and angle velocity were calculated using the instantaneous helical axis method and motion variables were extracted. A principal component analysis was performed in order to reduce data and improve the BPNN performance. BPNNs with six hidden nodes had a predictivity of 0.89, a sensitivity of 0.90 and a specificity of 0.88, which are very promising results. This shows that neck movement analysis combined with a neural network could build the basis of a decision support system for classifying suspected WAD, even though further evaluation of the method is needed.

  • 2.
    Listz Maurice, Roch
    et al.
    University of Montreal Hospital and University of Montreal, Canada.
    Fromageau, Jérémie
    University of Montreal Hospital, Canada .
    Roy Cardinal, Marie-Hélène
    University of Montreal Hospital, Canada .
    Doyley, Marvin
    Thayer School of Engineering, Dartmouth College, and Dartmouth Medical School, Hanover, NH, USA.
    de Muinck, Ebo
    Dartmouth Medical School, Hanover, NH, USA.
    Robb, John
    Dartmouth Medical School, Hanover, NH, USA.
    Cloutier, Guy
    University of Montreal Hospital and University of Montreal, Canada.
    Characterization of atherosclerotic plaques and mural thrombi with intravascular ultrasound elastography: A potential method evaluated in an aortic rabbit model and a human coronary artery2008In: IEEE transactions on information technology in biomedicine, ISSN 1089-7771, E-ISSN 1558-0032, Vol. 12, no 3, p. 290-298Article in journal (Refereed)
    Abstract [en]

    Plaque rupture is correlated with the plaque morphology, composition, mechanical properties, and with the blood pressure. Whereas the geometry can accurately be assessed with intravascular ultrasound (IVUS) imaging, intravascular elastography (IVE) is capable of extracting information on the plaque local mechanical properties and composition. This paper reports additional IVE validation data regarding reproducibility and potential to characterize atherosclerotic plaques and mural thrombi. In a first investigation, radio frequency (RF) data were acquired from the abdominal aorta of an atherosclerotic rabbit model. In a second investigation, IVUS RF data were recorded from the left coronary artery of a patient referred for angioplasty. In both cases, Galaxy IVUS scanners (Boston Scientific, Freemont, CA), equipped with 40 MHz Atlantis catheters, were used. Elastograms were computed using two methods, the Lagrangian speckle model estimator (LSME) and the scaling factor estimator (SFE). Corroborated with histology, the LSME and the SFE both clearly detected a soft thrombus attached to the vascular wall. Moreover, shear elastograms, only available with the LSME, confirmed the presence of the thrombus. Additionally, IVE was found reproducible with consistent elastograms between cardiac cycles (CCs). Regarding the human dataset, only the LSME was capable of identifying a plaque that presumably sheltered a lipid core. Whereas such an assumption could not be certified with histology, radial shear and tangential strain LSME elastograms enabled the same conclusion. It is worth emphasizing that this paper reports the first ever in vivo tangential strain elastogram with regards to vascular imaging, due to the LSME. It is concluded that the IVE was reproducible exhibiting consistent strain patterns between CCs. The IVE might provide a unique tool to assess coronary wall lesions.

  • 3.
    Pham, Tuan D.
    et al.
    School of Engineering and Information Technology, University of New South Wales, Canberra, Australia.
    Berger, Klaus
    Institute of Epidemiology and Social Medicine, University of Muenster, Germany.
    Automated detection of white matter changes in elderly people using fuzzy, geostatistical, and information combining models2011In: IEEE transactions on information technology in biomedicine, ISSN 1089-7771, E-ISSN 1558-0032, Vol. 15, no 2, p. 242-250Article in journal (Refereed)
    Abstract [en]

    Detection of white matter changes of the brain using magnetic resonance imaging (MRI) has increasingly been an active and challenging research area in computational neuroscience. There have rarely been any single image analysis methods that can effectively address the issue of automated quantification of neuroimages, which are subject to different interests of various medical hypotheses. This paper presents new image segmentation models for automated detection of white matter changes of the brain in an elderly population. The methods are based on the computational models of fuzzy clustering, possibilistic clustering, geostatistics, and knowledge combination. Experimental results on MRI data have shown that the proposed image analysis methodology can be applied as a very useful computerized tool for the validation of our particular medical question, where white matter changes of the brain are thought to be the most important social medical evidence.

  • 4.
    Öhberg, Fredrik
    et al.
    Department of Biomedical Engineering and Informatics, University Hospital, Umeå.
    Grip, Helena
    Department of Biomedical Engineering and Informatics, University Hospital, Umeå.
    Wiklund, Urban
    Department of Biomedical Engineering and Informatics, University Hospital, Umeå.
    Sterner, Ylva
    Community Medicine and Rehabilitation, University Hospital, Umeå, Sweden.
    Karlsson, J. Stefan
    Department of Biomedical Engineering and Informatics, University Hospital, Umeå.
    Gerdle, Björn
    Linköping University, Department of Clinical and Experimental Medicine, Rehabilitation Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Medicine, Pain and Rehabilitation Centre.
    Chronic Whiplash Associated Disorders and Neck Movement Measurements: An Instantaneous Helical Axis Approach2003In: IEEE transactions on information technology in biomedicine, ISSN 1089-7771, E-ISSN 1558-0032, Vol. 7, no 4, p. 274-282Article in journal (Refereed)
    Abstract [en]

    This paper presents an assessment tool for objective neck movement analysis of subjects suffering from chronic whiplash-associated disorders (WAD). Three-dimensional (3-D) motion data is collected by a commercially available motion analysis system. Head rotation, defined in this paper as the rotation angle around the instantaneous helical axis (IHA), is used for extracting a number of variables (e.g., angular velocity and range, symmetry of motion). Statistically significant differences were found between controls and subjects with chronic WAD in a number of variables.

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