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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here