Osteoporosis is loosely defined as a decrease in bone mineral density. Osteoporosis of the vertebral bodies is a significant risk factor for several complications following spinal surgery including proximal and distal junctional failure, pedicle screw pullout, and interbody cage subsidence. Bone mineral density has been established as a risk factor for many of these complications, and preoperative patient risk stratification may be critical to avoid their development. Historically, imaging scans have been manually segmented to assess sarcopenic muscle and osteoporotic bone to predict the risk of surgical complications following spine surgery. However, the manual assessment of osteoporosis in imaging scans is laborious, time intensive, and error prone. Our aim is to create an automated segmentation algorithm to assess bone mineral density specific to each patient and each vertebra. In the long term, our automated segmentation algorithm can be integrated into the clinic for improved patient selection and effective post-operative management. In the meantime, this platform can be integrated into pre-existing augmented reality systems to enable more precise pedicle screw placement via haptic feedback and intraoperative registration throughout the course of surgery.