Statistical Shape Model
Demonstration using the Osteoarthritis Initiative (OAI) Database,
18-R8072
Principal Investigators
Todd L. Bredbenner
Daniel P. Nicolella
Inclusive Dates: 06/03/09 – 10/05/09
Background - Osteoarthritis (OA) is the most common form of arthritis and the major cause of activity limitation and physical disability in older people. Today, 35 million people (13 percent of the U.S. population) are 65 and older, and more than half of them have radiological evidence of osteoarthritis in at least one joint. By 2030, about 70 million people will have passed their 65th birthday and will be at risk for OA. Before age 45, more men than women have osteoarthritis; after age 45, it is more common in women. It is also more likely to occur in people who are overweight and in those with jobs that stress particular joints. The absence of therapies to treat OA is, in part, a result of an incomplete understanding of the mechanisms underlying the development and progression of OA. However, it is widely believed that OA results from the local mechanical environment of the joint in general, and in the cartilage in particular, in combination with systemic susceptibility to the disease. Three dominant risk factors for early onset development of knee osteoarthritis are mechanical insult to the joint, ligament damage, and obesity, all of which alter the mechanical environment of the knee joint, and it is thought that this alteration in joint mechanics is in part responsible for the accelerated degradation of cartilage. However, many individuals who do not have these risk factors will go on to develop the disease later in life. This has led to the hypothesis that slight differences in joint mechanics, driven by variability in joint anatomy, along with biological predisposition, lead some individuals to develop OA while others do not.
Approach - The objective of this project was to apply SwRI-developed statistical shape modeling methods to a subset of patient datasets of the NIH OAI clinical MRI image data to determine the efficacy of distinguishing patients who will not develop (e.g., controls), are at risk of developing, or who have clinical signs of osteoarthritis.
Accomplishments - This preliminary study demonstrated quantitative differences in femur and tibia surface geometry between the control and incidence groups. Statistical shape modeling methodology was capable of efficiently describing variability in knee articular surface geometry. In combination with observed surface geometry differences in the tibia and femur, the lack of statistically significant differences in knee joint alignment measures for individual knees in the control and incidence groups suggests that variability in individual bone geometry may play a greater role in determining joint space geometry. The results of this preliminary study underscore the importance of considering geometry of the individual bones and other structures comprising the knee joint in advancing the understanding of osteoarthritis.
