Abstract #0693

Fracture Risk Assessment using Deep Learning and Hip Microarchitecture MRI

Cem M Deniz^1,2, Kyunghyun Cho³, Stephen Honig⁴, Kenneth A Egol⁵, Daniel K Sodickson^1,2, and Gregory Chang⁶

¹Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R) and Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, NY, United States, ²The Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, NY, United States, ³Courant Institute of Mathematical Sciences & Centre for Data Science, New York University, New York, NY, United States, ⁴Osteoporosis Center, Hospital for Joint Diseases, NYU Langone Medical Center, New York, NY, United States, ⁵Department of Orthopaedic Surgery, Hospital for Joint Diseases, NYU Langone Medical Center, New York, NY, United States, ⁶Department of Radiology, Center for Musculoskeletal Care, New York University Langone Medical Center, New York, NY, United States

The identification of subjects with high risk of developing osteoporosis-related fracture remains challenging. In this project, we developed supervised convolutional neural networks for hip fracture risk identification using proximal femur MR microarchitecture images and patients’ history of fragility fractures. We found that the proposed fracture risk assessment method provides superior discrimination of fragility fracture patients from controls compared to the current standard of care, DXA.

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