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Purpose: The study aims to identify differences in tibiofemoral joint morphology between responders (R group, no pain) to arthroscopic partial medial meniscectomy (APMM) versus medial postmeniscectomy syndrome patients (MPMS group, recurrent pain at 2 years postmeniscectomy) in a clinically neutrally aligned patient population. The second aim was to build a morphology-based predictive algorithm for response to treatment (RTT) in APMM. Methods: Two patient groups were identified from a large multicentre database of meniscectomy patients at 2 years of follow-up: the R group included 120 patients with a KOOS pain score > 75, and the MPMS group included 120 patients with a KOOS pain score ≤ 75. Statistical shape models (SSMs) of distal femur, proximal tibia and tibiofemoral joint were used to compare knee morphology. Finally, a predictive model was developed to predict RTT, with the SSM-derived morphologic variables as predictors. Results: No differences were found between the R and MPMS groups for patient age, sex, height, weight or cartilage status. Knees in the MPMS group were significantly smaller, had a wider femoral notch and a smaller medial femoral condyle. A morphology-based predictive model was able to predict MPMS at 2 years follow-up with a sensitivity of 74.9% (95% confidence interval [CI]: 74.4%-75.4%) and a specificity of 81.0% (95% CI: 80.6%-81.5%). Conclusion: A smaller tibiofemoral joint, a wider intercondylar notch and smaller medial femoral condyle were observed shape variations related to medial postmeniscectomy syndrome. These promising results are a first step towards a knee morphology-based clinical decision support tool for meniscus treatment. Study Design: Case-control study. Level of Evidence: Level IIIb.
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Manual anatomical landmarking for morphometric knee bone characterization in orthopedics is highly time-consuming and shows high operator variability. Therefore, automation could be a substantial improvement for diagnostics and personalized treatments relying on landmark-based methods. Applications include implant sizing and planning, meniscal allograft sizing, and morphological risk factor assessment. For twenty MRI-based 3D bone and cartilage models, anatomical landmarks were manually applied by three experts, and morphometric measurements for 3D characterization of the distal femur and proximal tibia were calculated from all observations. One expert performed the landmark annotations three times. Intra- and inter-observer variations were assessed for landmark position and measurements. The mean of the three expert annotations served as the ground truth. Next, automated landmark annotation was performed by elastic deformation of a template shape, followed by landmark optimization at extreme positions (highest/lowest/most medial/lateral point). The results of our automated annotation method were compared with ground truth, and percentages of landmarks and measurements adhering to different tolerances were calculated. Reliability was evaluated by the intraclass correlation coefficient (ICC). For the manual annotations, the inter-observer absolute difference was 1.53 ± 1.22 mm (mean ± SD) for the landmark positions and 0.56 ± 0.55 mm (mean ± SD) for the morphometric measurements. Automated versus manual landmark extraction differed by an average of 2.05 mm. The automated measurements demonstrated an absolute difference of 0.78 ± 0.60 mm (mean ± SD) from their manual counterparts. Overall, 92% of the automated landmarks were within 4 mm of the expert mean position, and 95% of all morphometric measurements were within 2 mm of the expert mean measurements. The ICC (manual versus automated) for automated morphometric measurements was between 0.926 and 1. Manual annotations required on average 18 min of operator interaction time, while automated annotations only needed 7 min of operator-independent computing time. Considering the time consumption and variability among observers, there is a clear need for a more efficient, standardized, and operator-independent algorithm. Our automated method demonstrated excellent accuracy and reliability for landmark positioning and morphometric measurements. Above all, this automated method will lead to a faster, scalable, and operator-independent morphometric analysis of the knee.
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PURPOSE: There is high variability in femoral torsion, measured on two-dimensional (2D) computed tomography (CT) scans. The aim of this study was to find a reliable three-dimensional (3D) femoral torsion measurement method, assess the influence of CAM deformity on femoral torsion measurement, and to promote awareness for the used measurement method. METHODS: 3D models of 102 dry femur specimens were divided into a CAM and non-CAM group. Femoral torsion was measured by one 2D-CT method described by Murphy et al. (method 0) and five 3D methods. The 3D methods differed in strategies to define the femoral neck axis. Method 1 is based on an elliptical least-square fit at the middle of the femoral neck. Methods 2 and 3 defined the centre of mass of the entire femoral neck and of the most cylindrical part, respectively. Methods 4 and 5 were based on the intersection of the femoral neck with a 25% and 40% enlarged best fit sphere of the femoral head. RESULTS: 3D methods resulted in higher femoral torsion measures than the 2D method; the mean torsion for method 0 was 8.12° ± 7.30°, compared to 9.93° ± 8.24° (p < 0.001), 13.21° ± 8.60° (p < 0.001), 8.21° ± 7.64° (p = 1.00), 9.53° ± 7.87° (p < 0.001) and 10.46° ± 7.83° (p < 0.001) for methods 1 to 5 respectively. In the presence of a CAM, torsion measured with method 4 is consistently smaller than measured with method 5. CONCLUSION: 2D measurement might underestimate true femoral torsion and there is a difference up to 5°. There is a tendency for a higher mean torsion in hips with a CAM deformity. Methods 4 and 5 are the most robust techniques. However, method 4 might underestimate femoral torsion if a CAM deformity is present. Since method 5 is independent of a CAM deformity, it is the preferred technique to define expected values of torsion.
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Purpose: To determine the extent of variability in meniscus size and anthropometric data between donors (supply) and patients (demand), to evaluate potential factors that may contribute to size discrepancies, and to determine whether the discrepancies lead to longer patient wait times. Methods: Lateral and medial meniscal measurements, anthropometric data, and time to match a donor graft were extracted from a tissue supplier database. The frequency and distribution of meniscus size were analyzed. Body mass index (BMI), relative meniscus area, body mass to meniscus area index, and height to meniscus area index were compared between patient and donor pools via χ2 tests and independent samples t-test. The effect of size on time to match was analyzed using analysis of variance and post-hoc Tukey test. Results: The lateral meniscus patient population showed a greater frequency of larger size requirements compared to the donor population (P < .001) and the medial meniscus patient population showed a higher frequency of smaller meniscus size requirements (P < .001). The medial meniscus analysis showed significantly smaller meniscus areas (P < .001) in the patient population contributing to the observed trend of an increased body mass to meniscus area index and height to meniscus area index. The time to match a donor meniscus was affected by the patient meniscus size. Conclusions: This analysis demonstrates variations in frequency of meniscus sizes between donor and patient populations. This variation is attributed to differences in anthropometric data between patient and donor populations. This work identifies a mismatch between demand and supply for certain patient sizes contributing to longer times to match. Clinical Relevance: This work associated donor and patient mismatches with longer wait times. This can be useful for patient counseling as well as provide a framework to determine whether there are solutions within the current meniscus donor pool that can be used to meet this clinical need.
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PURPOSE: Early-onset degeneration of the knee is linked to genetics, overload, injury, and potentially, knee morphology. The purpose of this study is to explore the characteristics of the small medial femoral condyle, as a distinct knee morphotype, by means of a landmark-based three-dimensional (3D) analysis and statistical parametric mapping. METHODS: Sixteen knees with a small medial femoral condyle (SMC) were selected from a database of patients with distinct knee joint anatomy and 16 gender-matched knees were selected from a control group database. 3D models were generated from the medical imaging. After normalization for size, a set of pre-defined landmark-based parameters was analysed for the femur and tibia. Local shape differences were evaluated by matching all bone surfaces onto each other and comparing the distances to the mean control group bone shape. RESULTS: The small medial condyle group showed a significant association with medial compartment degeneration and had a 4% and 13% smaller medial condyle anteroposteriorly and mediolaterally, whereas the distal femur was 3% wider mediolaterally. The lateral condyle was 2% smaller anteroposteriorly and 8% wider mediolaterally. The complete tibial plateau was 3% smaller mediolaterally and the medial tibial plateau was 6% smaller. CONCLUSION: A new knee morphotype demonstrated an increased risk for medial compartment degeneration and was differentiated from a healthy control group based on the following morphological characteristics: a smaller medial femoral condyle and medial tibial plateau, a wider lateral femoral condyle and a wider distal femur on a smaller tibial plateau. This pilot study suggests a role for the SMC knee morphotype in the multifactorial process of medial compartment degeneration. LEVEL OF EVIDENCE: III.