Contribution of Labrum and Cartilage to Joint Surface in Different Hip Deformities: An Automatic Deep Learning–Based 3-Dimensional Magnetic Resonance Imaging Analysis

Abstract

Background:

Multiple 2-dimensional magnetic resonance imaging (MRI) studies have indicated that the size of the labrum adjusts in response to altered joint loading. In patients with hip dysplasia, it tends to increase as a compensatory mechanism for inadequate acetabular coverage.

Purpose:

To determine the differences in labral contribution to the joint surface among different hip deformities as well as which radiographic parameters influence labral contribution to the joint surface using a deep learning–based approach for automatic 3-dimensional (3D) segmentation of MRI.

Study Design:

Cross-sectional study; Level of evidence, 4.

Methods:

This retrospective study was approved by the local ethics committee with waiver for informed consent. A total of 98 patients (100 hips) with symptomatic hip deformities undergoing direct hip magnetic resonance arthrography (3 T) between January 2020 and October 2021 were consecutively selected (mean age, 30 ± 9 years; 64% female). The standard imaging protocol included proton density–weighted turbo spin echo images and an axial-oblique 3D T1-weighted MP2RAGE sequence. According to acetabular morphology, hips were divided into subgroups: dysplasia (lateral center-edge [LCE] angle, <23°), normal coverage (LCE, 23°-33°), overcoverage (LCE, 33°-39°), severe overcoverage (LCE, >39°), and retroversion (retroversion index >10% and all 3 retroversion signs positive). A previously validated deep learning approach for automatic segmentation and software for calculation of the joint surface were used. The labral contribution to the joint surface was defined as follows: labrum surface area/(labrum surface area + cartilage surface area). One-way analysis of variance with Tukey correction for multiple comparison and linear regression analysis was performed.

Results:

The mean labral contribution of the joint surface of dysplastic hips was 26% ± 5% (95% CI, 24%-28%) and higher compared with all other hip deformities (P value range, .001-.036). Linear regression analysis identified LCE angle (β = −.002; P < .001) and femoral torsion (β = .001; P = .008) as independent predictors for labral contribution to the joint surface with a goodness-of-fit R² value of 0.35.

Conclusion:

The labral contribution to the joint surface differs among hip deformities and is influenced by lateral acetabular coverage and femoral torsion. This study paves the way for a more in-depth understanding of the underlying pathomechanism and a reliable 3D analysis of the hip joint that can be indicative for surgical decision-making in patients with hip deformities.