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Abstract #2685

 T2 Analysis of the Entire Osteoarthritis Initiative Dataset (N=25,729)

Alaleh Razmjoo1, Francesco Caliva1, Jinhee Lee1, Felix Liu2, Gabby B. Joseph1, Thomas M. Link 1, Sharmila Majumdar 1,3, and Valentina Pedoia 1,3
1Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States, 2Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, United States, 3Center of Digital Health Innovation (CDHI), University of California, San Francisco, San Francisco, CA, United States

Cartilage T2 relaxometry values are previously shown to be correlated to incidence OA, however prognostic ability of T2 is not yet established. In this study, an automatic deep learning method is built using 3921 manually segmented images and T2 was evaluated on entire Osteoarthritis Initiative Dataset (N=25,729). The proposed automatic T2 quantification was shown to be interchangeable with human process and significant association between elevated T2 and future incidence of OA was observed. The results of this study prove the prognostic ability of this compositional MRI technique on the larger sample ever analyzed.

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