Accuracy Tradeoffs between Individual Bone and Joint-Level Statistical Shape Models of Knee Morphology

Statistical shape models (SSMs) are useful tools in evaluating variation in bony anatomy to assess pathology, plan surgical interventions, and inform the design of orthopaedic implants and instrumentation. Recently, by considering multiple bones spanning a joint or the whole lower extremity, SSMs can support studies investigating articular conformity and joint mechanics. The objective of this study was to assess tradeoffs in accuracy between SSMs of the femur or tibia individually versus a combined joint-level model. Three statistical shape models were developed (femur-only, tibia-only, and joint-level) for a training set of 179 total knee arthroplasty (TKA) patients with osteoarthritis representing both genders and several ethnicities. Bone geometries were segmented from preoperative CT scans, meshed with triangular elements, and registered to a template for each SSM. Principal component analysis was performed to determine modes of variation. The statistical shape models were compared using measures of compactness, accuracy, generalization, and specificity. The generalization evaluation, assessing the ability to describe an unseen instance in a leave-one-out analysis, showed that errors were consistently smaller for the individual femur and tibia SSMs than for the joint-level model. However, when additional modes were included in the joint-level model, the errors were comparable to the individual bone results, with minimal additional computational expense. When developing more complex SSMs at the joint, lower limb, or whole-body level, the use of an error threshold to inform the number of included modes, instead of 95% of the variation explained, can help to ensure accurate representations of anatomy.