Background and Objective: As in vivo measurements of knee joint contact forces remain challenging, computational musculoskeletal modeling has been popularized as an encouraging solution for non-invasive estimation of joint mechanical loading. Computational musculoskeletal modeling typically relies on laborious manual segmentation as it requires reliable osseous and soft tissue geometry. To improve on feasibility and accuracy of patient-specific geometry predictions, a generic computational approach that can easily be scaled, morphed and fitted to patient-specific knee joint anatomy is presented.Methods: A personalized prediction algorithm was established to derive soft tissue geometry of the knee, originating solely from skeletal anatomy. Based on a MRI dataset (n = 53), manual identification of soft-tissue anatomy and landmarks served as input for our model by use of geometric morphometrics. Topographic distance maps were generated for cartilage thickness predictions. Meniscal modeling relied on wrapping a triangular geometry with varying height and width from the anterior to the posterior root. Elastic mesh wrapping was applied for ligamentous and patellar tendon path modeling. Leave-one-out validation experiments were conducted for accuracy assessment.Results: The Root Mean Square Error (RMSE) for the cartilage layers of the medial tibial plateau, the lateral tibial plateau, the femur and the patella equaled respectively 0.32 mm (range 0.14–0.48), 0.35 mm (range 0.16–0.53), 0.39 mm (range 0.15–0.80) and 0.75 mm (range 0.16–1.11). Similarly, the RMSE equaled respectively 1.16 mm (range 0.99–1.59), 0.91 mm (0.75–1.33), 2.93 mm (range 1.85–4.66) and 2.04 mm (1.88–3.29), calculated over the course of the anterior cruciate ligament, posterior cruciate ligament, the medial and the lateral meniscus.Conclusion: A methodological workflow is presented for patient-specific, morphological knee joint modeling that avoids laborious segmentation. By allowing to accurately predict personalized geometry this method has the potential for generating large (virtual) sample sizes applicable for biomechanical research and improving personalized, computer-assisted medicine.

No abstract

No abstract

Sacroiliac joint (SIJ) biomechanics have been described in both in vitro and in vivo studies. A standard for joint coordinate systems has been created by the International Society of Biomechanics for most of the joints in the human body. However, a standardized joint coordinate system for sacroiliac joint motion analysis is currently still lacking. This impedes the comparison across studies and hinders communication among scientists and clinicians. As SIJ motion is reported to be quite limited, a proper standardization and reproducibility of this procedure is essential for the interpretation of future biomechanical SIJ studies. This paper proposes a joint coordinate system for the analysis of sacroiliac joint motion, based on the procedure developed by Grood and Suntay, using semi-automated anatomical landmarks on 3D joint surfaces. This coordinate system offers high inter-rater reliability and aspires to a more intuitive representation of biomechanical data, as it is aligned with SIJ articular surfaces. This study aims to encourage further reflection and debate on biomechanical data representation, in order to facilitate interpretation of SIJ biomechanics and improve communication between researchers and clinicians. ß

No abstract

Several emerging reports suggest an important involvement of the hindfoot alignment in the outcome of knee osteotomy. At present, studies lack a comprehensive overview. Therefore, we aimed to systematically review all biomechanical and clinical studies investigating the role of the hindfoot alignment in the setting of osteotomies around the knee.A systematic literature search was conducted on multiple databases combining “knee osteotomy” and “hindfoot/ankle alignment” search terms. Articles were screened and included according to the PRISMA guidelines. A quality assessment was conducted using the Quality Appraisal for Cadaveric Studies (QUACS) - and modified methodologic index for non-randomized studies (MINORS) scales.Three cadaveric, fourteen retrospective cohort and two case-control studies were eligible for review. Biomechanical hindfoot characteristics were positively affected (n=4), except in rigid subtalar joint (n=1) or talar tilt (n=1) deformity. Patient symptoms and/or radiographic alignment at the level of the hindfoot did also improve after knee osteotomy (n=13), except in case of a small pre-operative lateral distal tibia- and hip knee ankle (HKA) angulation or in case of a large HKA correction (>14.5°). Additionally, a pre-existent hindfoot deformity (>15.9°) was associated with undercorrection of lower limb alignment following knee osteotomy. The mean QUACS score was 61.3% (range: 46–69%) and mean MINORS score was 9.2 out of 16 (range 6–12) for non-comparative and 16.5 out of 24 (range 15–18) for comparative studies.Osteotomies performed to correct knee deformity have also an impact on biomechanical and clinical outcomes of the hindfoot. In general, these are reported to be beneficial, but several parameters were identified that are associated with newly onset – or deterioration of hindfoot symptoms following knee osteotomy. Further prospective studies are warranted to assess how diagnostic and therapeutic algorithms based on the identified criteria could be implemented to optimize the overall outcome of knee osteotomy.Remark: Aline Van Oevelen and Arne Burssens contributed equally to this work

Background: To date, the amount of cartilage loss is graded by means of discrete scoring systems on artificially divided regions of interest (ROI). However, optimal statistical comparison between and within populations requires anatomically standardized cartilage thickness assessment. Providing anatomical standardization relying on non-rigid registration, we aim to compare morphotypes of a healthy control cohort and virtual reconstructed twins of end-stage knee OA subjects to assess the shape-related knee OA risk and to evaluate possible correlations between phenotype and location of cartilage loss.Methods: Out of an anonymized dataset provided by the Medacta company (Medacta International SA, Castel S. Pietro, CH), 798 end-stage knee OA cases were extracted. Cartilage wear patterns were observed by computing joint space width. The three-dimensional joint space width data was translated into a two-dimensional pixel image, which served as the input for a principal polynomial autoencoder developed for non-linear encoding of wear patterns. Virtual healthy twin reconstruction enabled the investigation of the morphology-related risk for OA requiring joint arthroplasty.Results: The polynomial autoencoder revealed 4 dominant, orthogonal components, accounting for 94% of variance in the latent feature space. This could be interpreted as medial (54.8%), bicompartmental (25.2%) and lateral (9.1%) wear. Medial wear was subdivided into anteromedial (11.3%) and posteromedial (10.4%) wear. Pre-diseased limb geometry had a positive predictive value of 0.80 in the prediction of OA incidence (r 0.58, p < 0.001).Conclusion: An innovative methodological workflow is presented to correlate cartilage wear patterns with knee joint phenotype and to assess the distinct knee OA risk based on pre-diseased lower limb morphology. Confirming previous research, both alignment and joint geometry are of importance in knee OA disease onset and progression.