Difference between revisions of "2016 Winter Project Week/Projects/StatisticalShapeModeling"

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Image:TMJ_OA_figure1.png|Different stages of bone resorption during TMJ OA.
 
Image:TMJ_OA_figure1.png|Different stages of bone resorption during TMJ OA.
 
Image:TMJ_OA_figure2.png|Mean morphology of each TMJ OA subgroups in orange compared to the healthy group in green.
 
Image:TMJ_OA_figure2.png|Mean morphology of each TMJ OA subgroups in orange compared to the healthy group in green.
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Image:UserInterfaceOAIndex.png|User's Interface of OAIndex.
 
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* Progress
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** Definition of the the best way to implement OAIndex
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** Definition of the user interface with Lucia Cevidanes and Beatriz Paniagua
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** Definition of how to use Statismo for OAIndex with Steve Pieper
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* Next Step
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** Definition of how display the result
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** Implementation of OAIndex
  
 
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Latest revision as of 12:57, 8 January 2016

Home < 2016 Winter Project Week < Projects < StatisticalShapeModeling

Osteoarthritis (OA), the most prevalent arthritis worldwide, is associated with significant pain and disability and affects 13.9% of adults at any given time. OA affects the temporomandibular joint (TMJ), among other joints. TMJ OA represents 42.6% of the diagnosed disorders of the TMJ, and results in $4 billion annual health care costs in the US. The complex pathogenesis of TMJ OA remains unclear to this day, and its course challenges experts given the different morphological patterns of bone resorption and formation observed in its various stages. The disease may evolve into repair and morphological adaptation, but also into aggressive bone destruction and functional impairment (see figure: Different stages of bone resorption during TMJ OA). We believe that quantitative tools to detect disease progression and disease staging is mandatory to characterize TMJ OA and to enable the development of effective treatments. We believe 3D Shape Analysis of the TMJ, altogether with Statistical Shape Modeling (SSM) can address the existing methodological gaps in TMJ OA monitoring by providing useful information about disease staging and disease progression.

TMJ OA joints were subdivided, by consensus between 2 clinicians, into 7 subgroups based in morphological variability compared to the average control morphology (see figure: Mean morphology of each TMJ OA subgroups).

http://www.shapesymposium.org/proceedings-screen.pdf (p15)

Key Investigators

  • François Budin, University of North Carolina
  • Lucia Cevidanes, University of Michigan
  • Beatriz Paniagua, University of North Carolina
  • Laura Pascal, University of Michigan
  • Steve Pieper, Isomics
  • Juan Carlos Prieto, University of North Carolina

Project Description

Objective Approach and Plan Progress and Next Steps
  • Work on the design of the CLI module OA Index which will automatically define the Temporomandibular Joint Osteoarthritis (TMJ OA) type of a patient
  • Define the user interface in according to the users specifications
  • Find the best way to implement OA Index by associating all the tools needed by the module (SPHARM, 3D Shape Analysis...)
  • Progress
    • Definition of the the best way to implement OAIndex
    • Definition of the user interface with Lucia Cevidanes and Beatriz Paniagua
    • Definition of how to use Statismo for OAIndex with Steve Pieper
  • Next Step
    • Definition of how display the result
    • Implementation of OAIndex