Post-traumatic osteoarthritis (PTOA) is a painful joint condition that occurs years after traumatic injury. Injuries to joints of the lower extremity most commonly result in PTOA, affecting over 5 million people in the United States alone. Characteristics of the condition include joint pain, stiffness, and functional limitations, as well as decreases in sleep quality and mental health. In the ankle joint specifically, compressive fractures of the distal tibia, or pilon fractures, provide opportunities to learn more about how the disease progresses, as these injuries result in high rates of PTOA development within a timeframe of 2-3 years.

By identifying early signs of PTOA, advancements can be made in testing new clinical treatments aimed at mitigating PTOA risk. Therefore, the goal of this project was to improve early detection of PTOA development by exploring relationships between variables used to quantify joint health and later indications of PTOA.

Previously developed computational modeling approaches were utilized to determine injury-specific fracture energy, to measure joint space width, and to estimate chronic contact stress exposure from pre-operative CT and post-operative weight bearing CT images. Modifications made to these modeling approaches improved JSW measurement accuracy and established the repeatability of these measurement techniques. Relationships were found between early classification metrics and subsequent joint changes indicative of PTOA that provide a basis for more reliable early detection of PTOA.