Abstract #3117

MRI-Based Deep Learning for Predicting Vertebral Fractures Risk in Patients with Low Bone Mass: a Multicenter Validation Study (n=1182)

Yi Yang¹, Tianyun Zhao^2,3, Qianyi Qiu¹, Qinglin Xie¹, Xinru Zhang¹, Jiayi Luo¹, Zhongping Zhang⁴, Yiou Wang¹, Jinling Wu¹, Chuan Huang^3,5, and Xiaodong Zhang¹

¹Department of Medical Imaging, The Third Affiliated Hospital, Southern Medical University, Guangzhou, China, ²Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, United States, ³Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, United States, ⁴Philips Healthcare China, Guangzhou, China, ⁵Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States

Synopsis

Keywords: Bone/Skeletal, Bone, paraspinal muscle

Motivation: Patients with low-energy vertebral fractures are typically at high risk for future fracture. Lumbar MRI-based deep learning (DL) can help improve prediction of fracture risk.

Goal(s): To develop and validate a DL model for fracture prediction based on vertebral and paraspinal muscular MR images.

Approach: Establish a DL model to predict fracture risk in low bone mass patients, including subgroup analyses for osteopenia and osteoporosis. Data was collected from three hospitals with an external validation set.

Results: In osteopenia, the combined vertebral-muscle model achieved an AUC of 0.648, outperforming the vertebral-only model’s 0.582 on the external validation set.

Impact: The paraspinal muscles, as one of the key structures in maintaining spinal stability, work synergistically with the vertebrae in predicting vertebral fracture risk, especially in osteopenia patients.

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