Introduction: Hip dysplasia, characterized by an insufficient acetabular coverage of the femoral head, increases hip joint stress and predisposes to degenerative changes. A novel 3D-printed, patient-specific extracapsular shelf implant was developed to increase femoral head coverage. Accurate implant placement is crucial. This cadaveric study compared CT-only surgical planning with combined CT- and MRI-based planning to evaluate whether MRI integration improves positioning accuracy. Methods: Two cohorts of non‑dysplastic cadaveric hips were studied. In cohort 1 (five hips), implant design was based solely on CT imaging. In cohort 2 (four hips), both CT and MRI datasets were used, incorporating capsular soft‑tissue anatomy. Postoperative implant positioning was compared with preoperative plans using point-cloud analyses, clockface coverage graphs, and Dice coefficients. Acceptable placement was defined as: median Euclidean distance < 5 mm, median angular deviation < 5°, and Dice > 0.75 respectively. Results: In the CT‑only cohort, three of five implants failed one or more accuracy thresholds, with Euclidean distances up to 8.5 mm, coverage deviations up to 6°, and Dice coefficients as low as 0.37. CT‑only designed implants consistently tilted away from the acetabular rim, reflecting underestimation of hip capsule thickness and insertion height. In the CT+MRI cohort, all four implants met the <5 mm and <5° deviation thresholds, and three achieved Dice ≥0.75. No consistent deviation patterns were observed. Conclusion: Combined CT and MRI planning improved implant positioning accuracy by better accounting for variations in hip capsule morphology. MRI integration demonstrated superior performance over CT‑only planning for patient‑specific shelf implant placement.

Objective: This study aimed to divide leg malalignment into different categories of valgus and varus of the femur, tibia, and intra-articular knee joint and investigates whether knee osteoarthritis (OA) patients are susceptible for changes of such leg deformities over time. Design: This study included 317 radiographs and CT-images on baseline and 24 months of 169 patients (median age 67, 78.2 % female) of the prospective European IMI-APPROACH cohort, enrolled for knee OA. Femoral, tibial, and intra-articular geometry was determined. Different categories were analysed based on varus or valgus in the femur, in the tibia, or within the intra-articular joint. Changes of these variables over time and their correlations were determined with mixed model analysis. Results: Femurs tended to become more varus-like over the two-year follow up (0.3°, 95 % CI 0.6°–0.1°, p = 0.02), bony valgus femurs became more varus shaped (1.1°, 95 % CI:1.7°–0.5°, p < 0.001). Patients with bone varus and a normal joint line convergence angle (JLCA) showed a significant increase in intra-articular joint varus, with a mean JLCA increase of 1.1°(95 % CI:0.4°–1.7°, p = 0.005). By two years, they reached the threshold for defining intra-articular joint varus deformity, with a JLCA of 2.0°. Conclusions: Substantial intra-articular joint and bone varus progression was observed within two years. This study shows that bone deformity is to some extent a dynamic process and there is a growing varus malalignment in the intra-articular knee joint and bones. Thereby this study emphasizes the importance of leg malalignment for progression of intra-articular knee joint changes in early OA.

Background/Objectives: Tenosynovitis is a common feature of psoriatic arthritis (PsA) and is typically assessed using semi-quantitative magnetic resonance imaging (MRI) scoring. However, visual scoring s variability. This study evaluates a fully automated, deep-learning approach for ankle tenosynovitis segmentation and volume-based quantification from MRI in psoriatic arthritis (PsA) patients. Methods: We analyzed 364 ankle 3T MRI scans from 71 PsA patients. Four tenosynovitis pathologies were manually scored and used to create ground truth segmentations through a human–machine workflow. For each pathology, 30 annotated scans were used to train a deep-learning segmentation model based on the nnUNet framework, and 20 scans were used for testing, ensuring patient-level disjoint sets. Model performance was evaluated using Dice scores. Volumetric pathology measurements from test scans were compared to radiologist scores using Spearman correlation. Additionally, 218 serial MRI pairs were assessed to analyze the relationship between changes in pathology volume and changes in visual scores. Results: The segmentation model achieved promising performance on the test set, with mean Dice scores ranging from 0.84 to 0.92. Pathology volumes correlated with visual scores across all test MRIs (Spearman ρ = 0.52–0.62). Volume-based quantification captured changes in inflammation over time and identified subtle progression not reflected in semi-quantitative scores. Conclusions: Our automated segmentation tool enables fast and accurate quantification of ankle tenosynovitis in PsA patients. It may enhance sensitivity to disease progression and complement visual scoring through continuous, volume-based metrics.

Purpose Hip osteoarthritis (OA) is a major cause of pain and disability worldwide. Lack of effective therapies may reflect poor knowledge on its aetiology and risk factors, and result in the management of end-stage hip OA with costly joint replacement. The Worldwide Collaboration on OsteoArthritis prediCtion for the Hip (World COACH) consortium was established to pool and harmonise individual participant data from prospective cohort studies. The consortium aims to better understand determinants and risk factors for the development and progression of hip OA, to optimise and automate methods for (imaging) analysis, and to develop a personalised prediction model for hip OA. Participants World COACH aimed to include participants of prospective cohort studies with ≥200 participants, that have hip imaging data available from at least 2 time points at least 4 years apart. All individual participant data, including clinical data, imaging (data), biochemical markers, questionnaires and genetic data, were collected and pooled into a single, individual-level database. Findings to date World COACH currently consists of 9 cohorts, with 38 021 participants aged 18–80 years at baseline. Overall, 71% of the participants were women and mean baseline age was 65.3±8.6 years. Over 34 000 participants had baseline pelvic radiographs available, and over 22 000 had an additional pelvic radiograph after 8–12 years of follow-up. Even longer radiographic follow-up (15–25 years) is available for over 6000 of these participants.

Introduction: To date the aetiology of femoroacetabular impingement (FAI) is still not completely understood. There are mechanical theories that suggest symptomatic FAI is linked to sagittal pelvic morphology and spinopelvic-femoral dynamics. The aim of this study is to evaluate the relation of sagittal pelvic morphology and orientation to radiographic signs of FAI. Additionally, we test whether the relation between FAI and spinopelvic parameters differs in osteoarthritic hips. Methods: From a prospective, observational cohort study, 1002 patients between 45 and 65 years old with a first episode of knee or hip pain were followed for 8 years. All patients who had lateral lumbar radiographs and clinical and radiographic follow-up of the hips were included in the present study. Range of internal rotation of the hip as well as radiographic signs of FAI (alpha and Wiberg angle) and presence of hip osteoarthritis (Kellgren and Lawrence) were systematically measured at baseline. Pelvic incidence (PI), pelvic tilt (PT), sacral slope (SS)) were measured at 8-year follow-up. Associations between PI, PT, SS and FAI parameters were tested using generalised estimating equations. Results: 421 subjects, 842 hips, were included. No significant relations between PI, PT or SS and alpha or Wiberg angle were found. Comparison of hips with and without radiological sign(s) of FAI showed no differences in PI, PT or SS. There was no relation between range of internal rotation of the hip and spinopelvic parameters. Conclusion: Sagittal pelvic morphology and orientation are not related to the presence of radiological signs of FAI in this study population.

Magnetic resonance Imaging is the gold standard for assessment of soft tissues; however, X-ray-based techniques are required for evaluating bone-related pathologies. This study evaluated the performance of synthetic computed tomography (sCT), a novel MRI-based bone visualization technique, compared with CT, for the scoring of knee osteoarthritis. sCT images were generated from the 3T T1-weighted gradient-echo MR images using a trained machine learning algorithm. Two readers scored the severity of osteoarthritis in tibiofemoral and patellofemoral joints according to OACT, which enables the evaluation of osteoarthritis, from its characteristics of joint space narrowing, osteophytes, cysts and sclerosis in CT (and sCT) images. Cohen's κ was used to assess the interreader agreement for each modality, and intermodality agreement of CT- and sCT-based scores for each reader. We also compared the confidence level of readers for grading CT and sCT images using confidence scores collected during grading. Inter-reader agreement for tibiofemoral and patellofemoral joints were almost-perfect for both modalities (κ = 0.83–0.88). The intermodality agreement of osteoarthritis scores between CT and sCT was substantial to almost-perfect for tibiofemoral (κ = 0.63 and 0.84 for the two readers) and patellofemoral joints (κ = 0.78 and 0.81 for the two readers). The analysis of diagnosis confidence scores showed comparable visual quality of the two modalities, where both are showing acceptable confidence levels for scoring OA. In conclusion, in this single-center study, sCT and CT were comparable for the scoring of knee OA.

Diagnosis of ankle impingement is performed primarily by clinical examination, whereas medical imaging is used for severity staging and treatment guidance. The association of impingement symptoms with regional three-dimensional (3D) bone shape variaties visible in medical images has not been systematically explored, nor do we know the type and magnitude of this relation. In this cross-sectional case-control study, we hypothesized that 3D talus bone shape could be used to quantitatively formulate the discriminating shape variations between ankles with impingement from ankles without impingement, and we aimed to characterize and quantify these variations. We used statistical shape modeling (SSM) methods to determine the most prevalent modes of shape variations that discriminate between the impinged and nonimpinged ankles. Results of the compactness and parallel analysis test on the statistical shape model identify 8 prominent shape modes of variations (MoVs) representing approximately 78% of the total 3D variations in the population of shapes, among which two modes captured discriminating features between impinged and nonimpinged ankles (p value of 0.023 and 0.042). Visual inspection confirms that these two shape modes, capturing abnormalities in the anterior and posterior parts of talus, represent the two main bony risk factors in anterior and posterior ankle impingement. In conclusion, in this research using SSM we have identified shape MoVs that were found to correlate significantly with bony ankle impingement. We also illustrated potential guidance from SSMs for surgical planning.