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Abstract #0198

Using Multidimensional Data Analysis to Identify Traits of Hip OA

Jasmine Rossi-deVries1, Valentina Pedoia1, Michael A Samaan1, Adam Ferguson1, Richard B Souza1, and Sharmila Majumdar1

1UCSF, San Francisco, CA, United States

This study aims to use big data analytics and imaging to simultaneously analyze all the combined variables in order to identify biomarkers able to classify the different disease progression of hip OA. 102 subjects and their 184 variables were examined. Big data analytics tool, Topological Data Analysis (TDA), was used to generate hypotheses. Three main groups were identified: healthy control subjects, subjects with radiographic and morphological evidence of OA, and subjects who progressed inconsistently were separated by knee biomechanics. The analysis obtained with TDA proposes new phenotypes of these subjects also shows the potential for further examination.

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