In a field where surgical outcomes are often unpredictable and chronic pain can persist despite multiple interventions, Morgan Giers is helping to reshape the landscape of spine care. As an assistant professor of bioengineering at Oregon State University and co-founder of the startup Spine by Design, Inc. Giers is leveraging predictive modeling, machine learning, and advanced imaging to bring precision and personalization to spinal treatment. Her research is rooted in a simple but powerful question: Can we match the right patient to the right procedure — and do it before the first incision is made? THE PROBLEM WITH SPINE SURGERY Spine surgery in the U.S. has a troubling track record. Procedures like spinal fusion and microdiscectomy often fail to relieve pain, with some patients undergoing multiple surgeries over numerous years — a population Giers refers to as “frequent flyers.” These patients bounce between specialists, often without lasting relief. “The average age for spine surgery is in the early forties,” Giers said. “These are people who still want to run marathons or lift their kids. When surgery fails, it’s not just disappointing — it’s life-altering.” PREDICTING REHERNIATION — AND PREVENTING IT One of Giers’ most impactful projects to date centers on reherniation after microdiscectomy, a common surgery involving the removal of the portion of a herniated disc compressing a nerve root. Her team analyzed data from over 350 patients, identifying key risk factors such as disc height index, body mass index, lumbar lordosis angle, herniation type, and smoking status. Using a regression model, they predicted with 97% accuracy which microdiscectomy surgeries resulted in reherniation. Furthermore, in a prospective study, Giers’ model helped reduce reherniation rates from 4% to 1%. But the results weren’t consistent across institutions — highlighting a deeper issue in spine care: measurement variability. ENGINEERING A SMARTER FUTURE FOR SPINE CARE 7 OREGON STATE ENGINEERING WINTER 2026
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