Distinct 3-Dimensional Morphologies of Arthritic Knee Anatomy Exist: CT-Based Phenotyping Offers Outlier Detection in Total Knee Arthroplasty

Abstract

Background: There is no foundational classification that 3-dimensionally characterizes arthritic anatomy to preoperatively plan and postoperatively evaluate total knee arthroplasty (TKA). With the advent of computed tomography (CT) as a preoperative planning tool, the purpose of this study was to morphologically classify pre-TKA anatomy across coronal, axial, and sagittal planes to identify outlier phenotypes and establish a foundation for future philosophical, technical, and technological strategies.

Methods: A cross-sectional analysis was conducted using 1,352 pre-TKA lower-extremity CT scans collected from a database at a single multicenter referral center. A validated deep learning and computer vision program acquired 27 lower-extremity measurements for each CT scan. An unsupervised spectral clustering algorithm morphometrically classified the cohort. The optimal number of clusters was determined through elbow-plot and eigen-gap analyses. Visualization was conducted through t-stochastic neighbor embedding, and each cluster was characterized. The analysis was repeated to assess how it was affected by severe deformity by removing impacted parameters and reassessing cluster separation.

Results: Spectral clustering revealed 4 distinct pre-TKA anatomic morphologies (18.5% Type 1, 39.6% Type 2, 7.5% Type 3, 34.5% Type 4). Types 1 and 3 embodied clear outliers. Key parameters distinguishing the 4 morphologies were hip rotation, medial posterior tibial slope, hip-knee-ankle angle, tibiofemoral angle, medial proximal tibial angle, and lateral distal femoral angle. After removing variables impacted by severe deformity, the secondary analysis again demonstrated 4 distinct clusters with the same distinguishing variables.

Conclusions: CT-based phenotyping established a 3D classification of arthritic knee anatomy into 4 foundational morphologies, of which Types 1 and 3 represent outliers present in 26% of knees undergoing TKA. Unlike prior classifications emphasizing native coronal plane anatomy, 3D phenotyping of knees undergoing TKA enables recognition of outlier cases and a foundation for longitudinal evaluation in a morphologically diverse and growing surgical population. Longitudinal studies that control for implant selection, alignment technique, and applied technology are required to evaluate the impact of this classification in enabling rapid recovery and mitigating dissatisfaction after TKA.

Level of evidence: Prognostic Level II. See Instructions for Authors for a complete description of levels of evidence.