Only 26% of Native Knees Show an Identical Coronal Functional Knee Phenotype in the Contralateral Knee.

Abstract

A comprehensive exploration evaluating left-to-right knee symmetry across all anatomical planes utilizing three-dimensional (3D) scans stands absent from the existing body of research. Therefore, the primary objectives of this investigation involved examining potential differences and resemblances in alignment and structure between left and right non-osteoarthritic (native) knees in various planes (coronal, sagittal, and axial) using three-dimensional single-photon emission computed tomography/computed tomography (SPECT/CT) images. A total of 282 native knees from 141 patients were retrospectively gathered from the hospital's records. Patients, aged between 16 and 45, who underwent Tc99m-methyl diphosphonate SPECT/CT scans for both knees, adhering to the Imperial Knee Protocol, were included. A statistical analysis was conducted, including 23 knee morphometric parameters, comparing left and right knees, and classifying them based on functional knee phenotypes across the coronal, sagittal, and axial planes. Regarding the functional coronal knee phenotype, 26% of patients (n = 37) exhibited identical phenotypes in both knees (p < 0.001). Significant correlated similarities between the left and right knees were observed in the coronal plane (Pearson's r = 0.76, 0.68, 0.76, 0.76, p < 0.001) and in several morphometric measures in the sagittal plane (Pearson's r = 0.92, 0.72, 0.64, p < 0.001). Moderately correlated similarities were noted in the axial plane (Pearson's r = 0.43, 0.44, 0.43, p < 0.001). Only 26% of native knees exhibit an identical coronal phenotype in their contralateral knee, whereas 67% have the adjacent coronal phenotype. Strongly correlated resemblances were established across various left and right knee morphometric parameters in the coronal, sagittal, and axial planes. These findings could enhance decisions in procedures like total knee arthroplasties or osteotomies, where alignment is key to outcomes, and reveal a potential for future artificial intelligence-driven models to improve our understanding and improve personalized treatment strategies for knee osteoarthritis.