The optimal neuromuscular control (muscle activation strategy that minimises the consumption of metabolic energy) during level walking is very close to that which minimises the force transmitted through the joints of the lower limbs. Thus, any suboptimal control involves an overloading of the joints. Some total knee replacement patients adopt suboptimal control strategies during level walking; this is particularly true for patients with co-morbidities that cause neuromotor control degeneration, such as Parkinson’s Disease (PD). The increase of joint loading increases the risk of implant failure, as reported in one study in PD patients (5.44% of failures at 9 years follow-up). One failure mode that is directly affected by joint loading is massive wear of the prosthetic articular surface. In this study we used a validated patient-specific biomechanical model to estimate how a severely suboptimal control could increase the wear rate of total knee replacements. Whereas autopsy-retrieved implants from non-PD patients typically show average polyethylene wear of 17 mm3 per year, our simulations suggested that a severely suboptimal control could cause a wear rate as high as of 69 mm3 per year. Assuming the risk of implant failure due to massive wear increase linearly with the wear rate, a severely suboptimal control could increase the risk associated to that failure mode from 0.1% to 0.5%. Based on these results, such increase would not be not sufficient to justify alone the higher incidence rate of revision in patients affected by Parkinson’s Disease, suggesting that other failure modes may be involved.

The manual segmentation of muscles on magnetic resonance images is the gold standard procedure to reconstruct muscle volumes from medical imaging data and extract critical information for clinical and research purposes. (Semi)automatic methods have been proposed to expedite the otherwise lengthy process. These, however, rely on manual segmentations. Nonetheless, the repeatability of manual muscle volume segmentations performed on clinical MRI data has not been thoroughly assessed. When conducted, volumetric assessments often disregard the hip muscles. Therefore, one trained operator performed repeated manual segmentations (n = 3) of the iliopsoas (n = 34) and gluteus medius (n = 40) muscles on coronal T1-weighted MRI scans, acquired on 1.5 T scanners on a clinical population of patients elected for hip replacement surgery. Reconstructed muscle volumes were divided in sub-volumes and compared in terms of volume variance (normalized variance of volumes – nVV), shape (Jaccard Index—JI) and surface similarity (maximal Hausdorff distance—HD), to quantify intra-operator repeatability. One-way repeated measures ANOVA (or equivalent) tests with Bonferroni corrections for multiple comparisons were conducted to assess statistical significance. For both muscles, repeated manual segmentations were highly similar to one another (nVV: 2–6%, JI > 0.78, HD < 15 mm). However, shape and surface similarity were significantly lower when muscle extremities were included in the segmentations (e.g., iliopsoas: HD –12.06 to 14.42 mm, P < 0.05). Our findings show that the manual segmentation of hip muscle volumes on clinical MRI scans provides repeatable results over time. Nonetheless, extreme care should be taken in the segmentation of muscle extremities.