The anterior cruciate ligament (ACL) is a vital knee stabilizer responsible for maintaining balance and function. ACL injuries are common and can cause prolonged pain, instability and an increased risk of post-traumatic osteoarthritis. Patient-specific variables, including age, graft choice and fixation method, have been shown to increase the likelihood of a surgical reconstruction failure, which can lead to additional pain and immobility. With so many variables to consider, accurately predicting a patient’s expected outcome is critical.
Kyle Martin, MD, an assistant professor in the Department of Orthopedic Surgery in the University of Minnesota Medical School, believes that machine learning could help predict a patient’s individualized outcomes more accurately and ultimately change how physicians choose to treat them. With the primary goal of creating an in-clinic calculator that physicians can use to predict the risk of ACL reconstruction failure at a patient-specific level, Dr. Martin’s project, “Robust predictive model for patient outcome following anterior cruciate ligament surgery (ACL) using the Norwegian Knee Ligament Registry (NLKR) and Machine Learning,” recently received grant funding through the Norwegian Centennial Chair Program.
“The funding represents a partnership between the Norwegian University of Life Sciences, University of Oslo and University of Minnesota Medical School to foster and encourage transatlantic collaboration on research studies,” Dr. Martin said.